Total laparoscopic distal gastrectomy with natural orifice specimen extraction (TLDG-NOSES) avoids the auxiliary incision needed in laparoscopic-assisted distal gastrectomy (LADG), potentially improving recovery. However, high-quality evidence on its safety and efficacy for distal gastric cancer surgery remains limited. Our study addresses this evidence gap by presenting the first multi-center, prospective, randomized controlled trial whether TLDG-NOSES improves postoperative recovery and reduces complications compared with LADG for distal gastric cancer. From April 2023 to January 2026, 232 patients with cT1-3N0-2M0 distal gastric adenocarcinoma were randomly assigned to TLDG-NOSES (n = 116) or LADG (n = 116). Operative time, blood loss, and lymph node yield were comparable between two groups (all P > 0.05). The overall complication rate was similar (6.9% vs. 8.6%; P = 0.624). Interestingly, TLDG-NOSES significantly shortened postoperative hospital stay (6.86 ± 2.82 vs. 8.38 ± 3.59 days; P < 0.001), accelerated first flatus (2.65 ± 1.39 vs. 3.39 ± 1.17 days; P<0.001) and liquid diet resumption (2.58 ± 0.97 vs. 3.69 ± 1.37 days; P < 0.001), and reduced pain on postoperative days 1-3 (all P < 0.05). Quality of life and cosmetic satisfaction were significantly better with TLDG-NOSES (both P < 0.001), while disease-free survival did not differ (P = 0.570), with no deaths in either group. TLDG-NOSES thus provides superior postoperative recovery, reduced pain, shorter hospital stay, and better quality of life without increasing complications, representing a safe and effective minimally invasive alternative for selected patients with distal gastric cancer, pending long-term oncological follow-up.

Abstract Background Ambulatory blood pressure monitoring objectively assesses circadian hemodynamic rhythms. However, the joint trajectories of longitudinal blood pressure (BP) and heart rate (HR), and their association with early hypertensive cardiac remodeling, remain incompletely characterized. Objective This study aimed to identify joint trajectories of BP and HR using group-based multitrajectory modeling and examine their associations with relative changes in relative wall thickness ( Δ RWT). Methods This community-based longitudinal cohort study included 213 patients with hypertension, with data collected between November 2022 and January 2025. Participants underwent 4 ambulatory BP monitoring sessions over 6 months, and echocardiography was measured at 3 months and 9 months. Group-based multitrajectory modeling was used to identify joint trajectories of 24-hour mean systolic blood pressure, diastolic blood pressure, and HR. A full-model multiple linear regression estimated the associations between trajectory groups and Δ RWT. Sensitivity analyses were conducted by adjusting for age and sex, and by using complete-case data. Results Four joint trajectory groups were identified: group 1 (lowest BP and HR, n=59), group 2 (normal BP, high HR, n=60), group 3 (stage 1 hypertension range, normal HR, n=54), and group 4 (stage 2 hypertension range, high HR, n=40). In the primary adjusted multiple linear regression model, compared with group 4, group 2 showed a significant inverse association with changes in Δ RWT ( b =−0.059, 95% CI −0.112 to −0.007; P =.03). Antidiabetic medication use was significantly associated with a reduction in Δ RWT ( b =−0.067, 95% CI −0.116 to −0.018; P =.008). Waking up before 7 AM was positively associated with an increase in Δ RWT ( b =0.045, 95% CI 0.007-0.083; P =.02). Notably, the association for group 2 attenuated to marginal significance ( P =.06) in the complete-case analysis, primarily due to the reduction in statistical power. Conclusions Hypertensive cardiac remodeling exhibits significant heterogeneity across distinct joint systolic blood pressure, diastolic blood pressure, and HR trajectories. Stable BP control is associated with a potential attenuation of early cardiac remodeling even in the presence of an elevated HR; however, this beneficial association attenuated to marginal significance in the complete-case analysis. Concurrent antidiabetic medication use and waking after 7 AM showed a beneficial association with the relative change in Δ RWT.

Cadmium exposure causes serious health consequences; however, there is no clinically approved antidote for cadmium poisoning. This Phase 1a/1b trial aimed to investigate safety, tolerability, and pharmacokinetics of Sodium (S)-2-(dithiocarboxylato((2S,3R,4R,5R)-2,3,4,5,6-pentahydroxyhexyl) amino)-4-(methylthio) butanoate (GMDTC), a novel chelating agent proved to eliminate cadmium through renal glucose transporters. This first-in-human study included two phases. Phase 1a was a randomized, double-blind, single-center, single-dose, dose escalation trial, in which 78 eligible healthy participants aged 18-65 years were assigned to cohorts receiving GMDTC (250, 500, 850, 1200, 1600, or 2000 mg) or placebo. Thereafter, Phase 1b randomized 30 participants with elevated urinary cadmium into three groups (500, 1000, or 2000 mg GMDTC), who received daily injections for 3 consecutive days, with placebo controls included in each group. During the trial, safety and tolerability were monitored for 72 hour following administration. The primary endpoints included dose-limiting toxicity, urinary cadmium excretion, pharmacokinetic parameters, and efficacy of GMDTC. In Phase 1a, 76 participants completed the study. No dose-limiting toxicities were observed, and 169 adverse events were recorded across all cohorts, with no apparent dose-related pattern or serious safety concerns. GMDTC exhibited dose-proportional pharmacokinetics, with a half-life of 1.25-2.63 hour, and urinary cadmium excretion increased dose-dependently within 24 hour after treatment. In Phase 1b, GMDTC significantly increased 24-hour urinary cadmium excretion, which positively correlated with the administered dose. GMDTC was well tolerated at doses up to 2000 mg, with no serious adverse events or dose-limiting toxicities. These findings support GMDTC as a novel therapeutic agent to enhance cadmium elimination in humans.

RATIONALE AND OBJECTIVES:Large hepatocellular carcinoma (HCC) exhibits heterogeneous morphologies and varied responses to treatment. We evaluated outcomes of patients with different large HCC classifications receiving surgical resection (SR) or transarterial chemoembolization plus ablation (TA). MATERIALS AND METHODS:Patients with HCC ≥ 5 cm receiving SR or TA between May 2016 and December 2020 at one center were analyzed retrospectively and with propensity score matching (PSM). Overall survival (OS) and progression-free survival (PFS) of the 2 treatment groups were compared. Tumors were classified according to imaging morphology and gross pathology: Type I, simple nodular; Type II, simple nodular with extranodular growth or confluent multinodular; Type III, infiltrative. RESULTS:Of 644 patients, 374 met the inclusion criteria (300 received SR and 74 received TA). Before PSM, median follow-up was 51.2 (IQR 29.6-65.3) months, and the SR group had longer OS (HR 2.13, 95% CI 1.44-3.15, p<0.001) and PFS (HR 2.31, 95% CI 1.66-3.20, p<0.001) than the TA group; after PSM these differences were not significant (all p>0.05). Infiltrative HCC (Type III) was an independent negative prognostic factor for OS and PFS. Within both treatment groups, patients with infiltrative HCC had shorter OS and PFS than patients with non-infiltrative HCC (Types I and II) (all p<0.001). CONCLUSION:For patients with HCC ≥ 5 cm, tumor classification is an important prognostic factor. In patients with non-infiltrative HCC, TA and SR had comparable OS after PSM. For patients with infiltrative HCC, TA and SR had limited efficacy.

Assuming a linear relationship between continuous predictors and outcomes in clinical prediction models is often inappropriate, as true linear relationships are rare, potentially resulting in biased estimates and inaccurate conclusions. Our research group addressed a single U-shaped independent variable before. Multiple U-shaped predictors can improve predictive accuracy by capturing nuanced relationships, but they also introduce challenges like increased complexity and potential overfitting. This study aims to extend the applicability of our previous research results to more common scenarios, thereby facilitating more comprehensive and practical investigations. In this study, we proposed a novel approach called the Recursive Gradient Scanning Method (RGS) for discretizing multiple continuous variables that exhibit U-shaped relationships with the natural logarithm of the odds ratio (lnOR). The RGS method involves a two-step approach: first, it conducts fine screening from the 2.5th to 97.5th percentiles of the lnOR. Then, it utilizes an iterative process that compares AIC metrics to identify optimal categorical variables. We conducted a Monte Carlo simulation study to investigate the performance of the RGS method. Different correlation levels, sample sizes, missing rates, and symmetry levels of U-shaped relationships were considered in the simulation process. To compare the RGS method with other common approaches (such as median, Q1-Q3, minimum P-value method), we assessed both the predictive ability (e.g., AUC) and goodness of fit (e.g., AIC) of logistic regression models with variables discretized at different cut-points using a real dataset. Both simulation and empirical studies have consistently demonstrated the effectiveness of the RGS method. In simulation studies, the RGS method showed superior performance compared to other common discretization methods in discrimination ability and overall performance for logistic regression models across various U-shaped scenarios (with varying correlation levels, sample sizes, missing rates, and symmetry levels of U-shaped relationships). Similarly, empirical study showed that the optimal cut-points identified by RGS have superior clinical predictive power, as measured by metrics such as AUC, compared to other traditional methods. The simulation and empirical study demonstrated that the RGS method outperformed other common discretization methods in terms of goodness of fit and predictive ability. However, in the future, we will focus on addressing challenges related to separation or missing binary responses, and we will require more data to validate our method.

Smoking is a pivotal modifiable risk factor for lung cancer (LC). Previous studies have indicated that a smoking cessation program might be incorporated into the LC screening program. However, the effects of smoking cessation and its duration with the age at onset (AAO) of LC, all-cause mortality, and LC-specific mortality remain unclear. We aimed to comprehensively investigate the association of smoking cessation-related behaviors on the AAO of LC, LC-specific and all-cause mortality. A total of 2671 smokers with LC as the primary site from the UK Biobank were included in this study, with a 7:3 ratio assigned randomly to a discovery set (n = 1872) and a validation set (n = 799). Generalized linear regression models were used for AAO of LC outcomes and Cox models for mortality outcomes. Participants over 60 years old could still benefit from smoking cessation to prolong AAOs (β = 1.613 for men, P = 0.003; β = 1.533 for women, P = 0.018). A cessation duration of > 15 years was associated with a later AAO in men (P < 0.001). Moreover, smoking cessation before 60 years old, especially among those under 40 years, was significantly associated with a lower risk of all-cause mortality (men: hazard ratio (HR): 0.65 [95

Background Limited research has investigated the influence of patient engagement on the long-term effects of mobile health (mHealth) interventions, particularly among older adults. Objective This study aimed to examine the long-term impact of a social media–driven mHealth intervention on blood pressure control among older Chinese individuals with hypertension, through repeated measurements of patient engagement and outcomes at 5 preset time points. Methods The study included older Chinese individuals with hypertension between 2017 and 2022. Participants received a hypertension self-management program via the WeChat social media app (Tencent Holdings Ltd), which provided clinically based digital coaching. Blood pressure measurements were taken repeatedly using a home blood pressure monitor (HBPM) connected to the app at baseline, 3, 6, 9, and 12 months. Patient engagement was evaluated based on the frequency of completed measurements at corresponding follow-ups. Latent growth curve models (LGCMs) served to assess the impact of patient engagement on blood pressure among older individuals with hypertension across preset points. Results A total of 1723 patients completed the 12-month follow-up (average age 70.1, SD 6.8 years; 890/1723, 51.7% female; and baseline systolic blood pressure 137.2 mm Hg). LGCMs revealed systolic blood pressure decreased significantly over 1 year, notably at 9 months (131 mm Hg, β 9 =3.244, P <.001), and continued up to 12 months (131.6mm Hg, β 12 =2.827, P <.001). In addition, a higher frequency of completed measurements was associated with better systolic blood pressure control at 3, 6, 9, and 12 months (β 3 =–0.016, P =.002; β 6 =–0.006, P =.02; β 9 =–0.002, P =.44; β 12 =–0.003, P =.02). These results remained significant even after accounting for age, sex, and comorbidity status. Conclusions This study, using LGCMs and repeated measures data, revealed a significant positive impact of patient engagement on long-term blood pressure control in mHealth interventions targeting older individuals with hypertension. These findings stress the importance of integration of patient-centered engagement approach into mHealth programs designed for chronic disease management in aging populations.

BACKGROUND:Although the number of clinical trials on lung cancer is rapidly increasing, the clinical benefits have not received sufficient attention. This study aims to assess the clinical benefits and estimate the minimal clinically important differences (MCIDs) for overall survival (OS) and progression-free survival (PFS) to provide quantitative guidance for treatment decisions and study design in lung cancer trials. METHODS:This study systematically searched lung cancer randomized controlled trials (RCTs) from PubMed, Embase, and the Cochrane Library. The clinical benefits were estimated using the frameworks of the European Society for Medical Oncology - Magnitude of Clinical Benefit Scale (ESMO-MCBS) and the American Society of Clinical Oncology - Value Framework (ASCO-VF). The MCIDs for OS and PFS were calculated using the distribution-based method. The differences in clinical benefits of lung cancer trials between the MCIDs and the two frameworks were compared. RESULTS:A total of 319 lung cancer RCTs were included. The mean improved OS and PFS were 2.28 and 1.76 months between the interventional and control groups, respectively. Around 15.79% of trials with OS as the primary endpoint were rated as grades 4 or above, and only 6.02% of trials with PFS as the primary endpoint reached grade 4 by ESMO-MCBS. The overall MCIDs for OS and PFS in non-small cell lung cancer (NSCLC) were 7.66 and 3.11 months, while 2.29 and 1.13 months in small cell lung cancer (SCLC), respectively. A total of 79.61% of RCTs evaluating OS (5.92% with clinical benefits and 73.68% without clinical benefits) and 68.26% evaluating PFS (3.59% with clinical benefits and 64.67% without clinical benefits) were consistently identified as reaching clinical benefits by the MCIDs and two frameworks. CONCLUSIONS:Although lung cancer RCTs showed statistically significant improvement in OS and PFS, most trials did not show clinical benefits, with a limited increase in survival months. A fair-to-moderate consistency of clinical benefits classification was observed between the MCIDs and the two frameworks. Further explorations into MCID are anticipated to deepen our understanding and promote its application in the future.

Background: Early identification of left ventricular ejection fraction (LVEF) levels during the progression of hypertension is essential to prevent cardiac deterioration. However, achieving a non-invasive, cost-effective, and definitive assessment is challenging. It has prompted us to develop a comprehensive machine learning framework for the automatic quantitative estimation of LVEF levels from electrocardiography (ECG) signals. Methods: We enrolled 200 hypertensive patients from Zhongshan City, Guangdong Province, China, from 1 November 2022 to 1 January 2025. Participants underwent 24 h Holter monitoring and echocardiography for LVEF estimation. We developed a comprehensive machine learning framework that initiated with preprocessed ECG signal in one-hour intervals to extract CMSE-based heart rate variability (HRV) features, then utilized machine learning models such as linear regression (LR), Support Vector Machines (SVMs), and random forests (RFs) with recursive feature elimination for optimal LVEF estimation. Results: The LR model, notably during early night interval (20:00–21:00), achieved a RMSE of 4.61% and a MAE of 3.74%, highlighting its superiority. Compared with other similar studies, key CMSE parameters (Scales 1, 5, Slope 1–5, and Area 1–5) can effectively enhance regression models’ estimation performance. Conclusion: Our findings suggest that CMSE-derived circadian HRV features from Holter ECG could serve as a non-invasive, cost-effective, and interpretable solution for LVEF assessment in community settings. From a machine learning interpretable perspective, the proposed method emphasized CMSE’s clinical potential in capturing autonomic dynamics and cardiac function fluctuations.

BackgroundAs a clinical precursor to Alzheimer’s disease (AD), amnestic mild cognitive impairment (aMCI) bears a considerably heightened risk of transitioning to AD compared to cognitively normal elders. Early prediction of whether aMCI will progress to AD is of paramount importance, as it can provide pivotal guidance for subsequent clinical interventions in an early and effective manner.MethodsA total of 107 aMCI cases were enrolled and their electroencephalogram (EEG) data were collected at the time of the initial diagnosis. During 18-month follow-up period, 42 individuals progressed to AD (PMCI), while 65 remained in the aMCI stage (SMCI). Spectral, nonlinear, and functional connectivity features were extracted from the EEG data, subjected to feature selection and dimensionality reduction, and then fed into various machine learning classifiers for discrimination. The performance of each model was assessed using 10-fold cross-validation and evaluated in terms of accuracy (ACC), area under the curve (AUC), sensitivity (SEN), specificity (SPE), positive predictive value (PPV), and F1-score.ResultsCompared to SMCI patients, PMCI patients exhibit a trend of “high to low” frequency shift, decreased complexity, and a disconnection phenomenon in EEG signals. An epoch-based classification procedure, utilizing the extracted EEG features and k-nearest neighbor (KNN) classifier, achieved the ACC of 99.96%, AUC of 99.97%, SEN of 99.98%, SPE of 99.95%, PPV of 99.93%, and F1-score of 99.96%. Meanwhile, the subject-based classification procedure also demonstrated commendable performance, achieving an ACC of 78.37%, an AUC of 83.89%, SEN of 77.68%, SPE of 76.24%, PPV of 82.55%, and F1-score of 78.47%.ConclusionAiming to explore the EEG biomarkers with predictive value for AD in the early stages of aMCI, the proposed discriminant framework provided robust longitudinal evidence for the trajectory of the aMCI cases, aiding in the achievement of early diagnosis and proactive intervention.

The clinical manifestations of ischemic cardiomyopathy (ICM) bear resemblance to dilated cardiomyopathy (DCM), yet their treatments and prognoses are quite different. Early differentiation between these conditions yields positive outcomes, but the gold standard (coronary angiography) is invasive. The potential use of ECG signals based on variational mode decomposition (VMD) as an alternative remains underexplored. An ECG dataset containing 87 subjects (44 DCM, 43 ICM) is pre-processed for denoising and heartbeat division. Firstly, the ECG signal is processed by empirical mode decomposition (EMD) and VMD. And then, five modes are determined by correlation analysis. Secondly, bispectral analysis is conducted on these modes, extracting corresponding bispectral and nonlinear features. Finally, the features are processed using five machine learning classification models, and a comparative assessment of their classification efficacy is facilitated. The results show that the technique proposed provides a better categorization for DCM and ICM using ECG signals compared to previous approaches, with a highest classification accuracy of 98.30%. Moreover, VMD consistently outperforms EMD under diverse conditions such as different modes, leads, and classifiers. The superiority of VMD on ECG analysis is verified.

Background: Autism Spectrum Disorder (ASD) is a complex neurodevelopment disease characterized by impaired social and cognitive abilities. Despite its prevalence, reliable biomarkers for identifying individuals with ASD are lacking. Recent studies have suggested that alterations in the functional connectivity of the brain in ASD patients could serve as potential indicators. However, previous research focused on static functional-connectivity analysis, neglecting temporal dynamics and spatial interactions. To address this gap, our study integrated dynamic functional connectivity, local graph-theory indicators, and a feature-selection and ranking approach to identify biomarkers for ASD diagnosis. Methods: The demographic information, as well as resting and sleeping electroencephalography (EEG) data, were collected from 20 ASD patients and 25 controls. EEG data were pre-processed and segmented into five sub-bands (Delta, Theta, Alpha-1, Alpha-2, and Beta). Functional-connection matrices were created by calculating coherence, and static-node-strength indicators were determined for each channel. A sliding-window approach, with varying widths and moving steps, was used to scan the EEG series; dynamic local graph-theory indicators were computed, including mean, standard deviation, median, inter-quartile range, kurtosis, and skewness of the node strength. This resulted in 95 features (5 sub-bands × 19 channels) for each indicator. A support-vector-machine recurrence-feature-elimination method was used to identify the most discriminative feature subset. Results: The dynamic graph-theory indicators with a 3-s window width and 50% moving step achieved the highest classification performance, with an average accuracy of 95.2%. Notably, mean, median, and inter-quartile-range indicators in this condition reached 100% accuracy, with the least number of selected features. The distribution of selected features showed a preference for the frontal region and the Beta sub-band. Conclusions: A window width of 3 s and a 50% moving step emerged as optimal parameters for dynamic graph-theory analysis. Anomalies in dynamic local graph-theory indicators in the frontal lobe and Beta sub-band may serve as valuable biomarkers for diagnosing autism spectrum disorders.

BACKGROUND:Over 60% of epilepsy patients globally are children, whose early diagnosis and treatment are critical for their development and can substantially reduce the disease's burden on both families and society. Numerous algorithms for automated epilepsy detection from EEGs have been proposed. Yet, the occurrence of epileptic seizures during an EEG exam cannot always be guaranteed in clinical practice. Models that exclusively use seizure EEGs for detection risk artificially enhanced performance metrics. Therefore, there is a pressing need for a universally applicable model that can perform automatic epilepsy detection in a variety of complex real-world scenarios.METHOD:To address this problem, we have devised a novel technique employing a temporal convolutional neural network with self-attention (TCN-SA). Our model comprises two primary components: a TCN for extracting time-variant features from EEG signals, followed by a self-attention (SA) layer that assigns importance to these features. By focusing on key features, our model achieves heightened classification accuracy for epilepsy detection.RESULTS:The efficacy of our model was validated on a pediatric epilepsy dataset we collected and on the Bonn dataset, attaining accuracies of 95.50% on our dataset, and 97.37% (A v. E), and 93.50% (B vs E), respectively. When compared with other deep learning architectures (temporal convolutional neural network, self-attention network, and standardized convolutional neural network) using the same datasets, our TCN-SA model demonstrated superior performance in the automated detection of epilepsy.CONCLUSION:The proven effectiveness of the TCN-SA approach substantiates its potential as a valuable tool for the automated detection of epilepsy, offering significant benefits in diverse and complex real-world clinical settings.

BackgroundDespite advancements in cancer treatment, understanding the long-term mental health implications for nasopharyngeal carcinoma (NPC) survivors remains an underexplored area. This study aims to examine the prevalence of mental disorders and their correlations with age at diagnosis and time since diagnosis among NPC survivors.MethodsA total of 1872 NPC patients were surveyed from September 2020 to June 2021 in this cross-sectional survey. Logistic regression models were used to analyze the associations of age at diagnosis and time since NPC diagnosis with the risk of mental disorders. Additionally, the potential nonlinear trend between these factors was examined using restricted cubic splines. Analyses were conducted both overall and stratified by gender. Gender interaction was also examined.ResultsThe prevalences of depression, anxiety, and sleep disorders were 32.4, 33.2, and 61.5%, respectively. Age at NPC diagnosis was significantly associated with an elevated risk of depression (adjusted OR (aOR): 1.75 for 30–39 years old; 2.33 for 50–59 years old; 2.59 for ≥60 years old) and sleep disorders (aOR: 2.41 for 40–49 years old; 1.95 for 50–59 years old; 2.26, for ≥60 years old), compared to patients diagnosed with NPC at age < 30 years. Conversely, the risk of depression, anxiety, and sleep disorders exhibited negative associations with the time since diagnosis, compared to patients <3 months. Notably, significant nonlinear associations were observed between time since diagnosis and the risk of depression, anxiety, and sleep disorders, which showed an initial increase, with the highest risk occurring at approximately 3.0 (ORmax: 2.7), 1.5 (ORmax: 2.1), and 4.0 (ORmax: 1.9) months since NPC diagnosis, followed by a gradual recovery to a lower risk level at around 12 months. No gender interactions were observed.ConclusionThe prevalence of mental disorders is notable among NPC survivors, showing a positive correlation with age at diagnosis while displaying a negative correlation with time since diagnosis, thus indicating the need for psychological support, especially within the initial several months following NPC diagnosis.

Traditional total mesorectal excision (TME) for rectal cancer requires partial resection of Denonvilliers’ fascia (DVF), which leads to injury of pelvic autonomic nerve and postoperative urogenital dysfunction. It is still unclear whether entire preservation of DVF has better urogenital function and comparable oncological outcomes. We conducted a randomized clinical trial to investigate the superiority of DVF preservation over resection (NCT02435758). A total of 262 eligible male patients were randomized to Laparoscopic TME with DVF preservation (L-DVF-P group) or resection procedures (L-DVF-R group), 242 of which completed the study, including 122 cases of L-DVF-P and 120 cases of L-DVF-R. The initial analysis of the primary outcomes of urogenital function has previously been reported. Here, the updated analysis and secondary outcomes including 3-year survival (OS), 3-year disease-free survival (DFS), and recurrence rate between the two groups are reported for the modified intention-to-treat analysis, revealing no significant difference. In conclusion, L-DVF-P reveals better postoperative urogenital function and comparable oncological outcomes for male rectal cancer patients.

Sperm quality can be easily influenced by living environmental and occupational factors. This study aimed to discover potential semen quality related living environmental and occupational factors, expand knowledge of risk factors for semen quality, strengthen men's awareness of protecting their own fertility and assist the clinicians to judge the patient’s fertility. 465 men without obese or underweight (18.5 < BMI < 28.5 kg/m 2 ), long-term medical history and history of drug use, were recruited between June 2020 to July 2021, they are in reproductive age (25 < age < 45 years). We have collected their semen analysis results and clinical information. Logistic regression was applied to evaluate the association of semen quality with different factors. We found that living environment close to high voltage line (283.4 × 10 6 /ml vs 219.8 × 10 6 /ml, Cohen d = 0.116, P = 0.030) and substation (309.1 × 10 6 /ml vs 222.4 × 10 6 /ml, Cohen d = 0.085, P = 0.015) will influence sperm count. Experienced decoration in the past 6 months was a significant factor to sperm count (194.2 × 10 6 /ml vs 261.0 × 10 6 /ml, Cohen d = 0.120, P = 0.025). Living close to chemical plant will affect semen PH (7.5 vs 7.2, Cohen d = 0.181, P = 0.001). Domicile close to a power distribution room will affect progressive sperm motility (37.0% vs 34.0%, F = 4.773, Cohen d = 0.033, P = 0.030). Using computers will affect both progressive motility sperm (36.0% vs 28.1%, t = 2.762, Cohen d = 0.033, P = 0.006) and sperm total motility (57.0% vs 41.0%, Cohen d = 0.178, P = 0.009). After adjust for potential confounding factors (age and BMI), our regression model reveals that living close to high voltage line is a risk factor for sperm concentration (Adjusted OR 4.03, 95% CI 1.15–14.18, R 2 = 0.048, P = 0.030), living close to Chemical plants is a protective factor for sperm concentration (Adjusted OR 0.15, 95% CI 0.05–0.46, R 2 = 0.048, P = 0.001) and total sperm count (Adjusted OR 0.36, 95% CI 0.13–0.99, R 2 = 0.026, P = 0.049). Time spends on computer will affect sperm total motility (Adjusted OR 2.29, 95% CI 1.11–4.73, R 2 = 0.041, P = 0.025). Sum up, our results suggested that computer using, living and working surroundings (voltage line, substation and chemical plants, transformer room), and housing decoration may association with low semen quality. Suggesting that some easily ignored factors may affect male reproductive ability. Couples trying to become pregnant should try to avoid exposure to associated risk factors. The specific mechanism of risk factors affecting male reproductive ability remains to be elucidated.

Background Observational studies have suggested U-shaped relationships between sleep duration and systolic blood pressure (SBP) with risks of many cardiovascular diseases (CVDs), but the cut-points that separate high-risk and low-risk groups have not been confirmed. We aimed to examine the U-shaped relationships between sleep duration, SBP, and risks of CVDs and confirm the optimal cut-points for sleep duration and SBP. Methods A retrospective analysis was conducted on NHANES 2007–2016 data, which included a nationally representative sample of participants. The maximum equal-odds ratio (OR) method was implemented to obtain optimal cut-points for each continuous independent variable. Then, a novel “recursive gradient scanning method” was introduced for discretizing multiple non-monotonic U-shaped independent variables. Finally, a multivariable logistic regression model was constructed to predict critical risk factors associated with CVDs after adjusting for potential confounders. Results A total of 26,691 participants (48.66% were male) were eligible for the current study with an average age of 49.43 ± 17.69 years. After adjusting for covariates, compared with an intermediate range of sleep duration (6.5–8.0 h per day) and SBP (95–120 mmHg), upper or lower values were associated with a higher risk of CVDs [adjusted OR (95% confidence interval) was 1.20 (1.04–1.40) for sleep duration and 1.17 (1.01–1.36) for SBP]. Conclusions This study indicates U-shaped relationships between SBP, sleep duration, and risks of CVDs. Both short and long duration of sleep/higher and lower BP are predictors of cardiovascular outcomes. Estimated total sleep duration of 6.5–8.0 h per day/SBP of 95–120 mmHg is associated with lower risk of CVDs.

Background: Postoperative nausea and vomiting (PONV) is a major problem after surgery. Even with double prophylactic therapy including dexamethasone and a 5-hydroxytryptamine-3 receptor antagonist, the incidence is still high in many at-risk patients. Fosaprepitant, a neurokinin-1 receptor antagonist, is an effective antiemetic, but its efficacy and safety in combination antiemetic therapy for preventing PONV remain unclear.Methods: In this randomised, controlled, double-blind trial, 1154 participants at high risk of PONV and undergoing laparoscopic gastrointestinal surgery were randomly assigned to either a fosaprepitant group (n=577) receiving fosaprepitant 150 mg i.v. dissolved in 0.9% saline 150 ml, or a placebo group (n=577) receiving 0.9% saline 150 ml before anaesthesia induction. Dexamethasone 5 mg i.v. and palonosetron 0.075 i.v. mg were each administered in both groups. The primary outcome was the incidence of PONV (defined as nausea, retching, or vomiting) during the first 24 postoperative hours.Results: The incidence of PONV during the first 24 postoperative hours was lower in the fosaprepitant group (32.4% vs 48.7%; adjusted risk difference-16.9% [95% confidence interval:-22.4 to-11.4%]; adjusted risk ratio 0.65 [95% CI: 0.57 to 0.76]; P<0.001). There were no differences in severe adverse events between groups, but the incidence of intraoperative hypotension was higher (38.0% vs 31.7%, P=0.026) and intraoperative hypertension (40.6% vs 49.2%, P=0.003) was lower in the fosaprepitant group.Conclusions: Fosaprepitant added to dexamethasone and palonosetron reduced the incidence of PONV in patients at high risk of PONV undergoing laparoscopic gastrointestinal surgery. Notably, it increased the incidence of intraoperative hypotension.Clinical trial registration: NCT04853147.

Blood pressure has a 24-hour repetitive and regular variation which shows circadian rhythm. Using the multivariate time series analysis method of vector autoregressive model, we could realize the simultaneous prediction for both systolic and diastolic blood pressures. We choose blood pressure from 6 AM to 10 AM in 3 weeks as an episode to construct a prediction model. Missing values were imputed by regression models. Subsequently, we defined segments as positive or negative segments according to blood pressure measurements. The predictions were accomplished by vector autoregressive model (VAR). Both positive and negative segments were randomly selected from each patient to summarize the effect of prediction models. In this study, the MAPE (Mean Absolute Percentage Error) of systolic blood pressure and diastolic blood pressure were both less than 10%, indicating that the VAR model was adaptable in predicting the blood pressure of hypertensive patients. Based on VAR, we could provide early warning to breakthrough of blood pressure thresholds. The sensitivity, specificity, and accuracy for patients in the training sets were 77.50%, 81.58 %, and 79.49% respectively, and the sensitivity, specificity, and accuracy for patients in the training sets were 76.92%, 80.00% and 78.43% respectively. This research took information of both systolic and diastolic blood pressures at the same time to establish the VAR models and enabled simultaneous prediction for systolic and diastolic blood pressure.