Clinical manifestations of brucellosis in humans are variable and nonspecific, therefore laboratory confirmation for appropriate treatment of the patient is essential. Brucella infection can be diagnosed by culture, serological tests, and nucleic acid amplification−based methods. The present study was conducted to investigate the molecular and seroepidemiological aspects of clinically suspected brucellosis patients in northeastern Iran. A total of 800 blood and serum samples were obtained from individuals presenting to medical centers in eastern and northeastern cities of Iran with clinical manifestations suggestive of brucellosis, including fever and chills, night sweats, recurrent and prolonged fever, weight loss, anorexia, and joint inflammation. The patients comprised of 66% men and 34% women, ranging in age from 12 to 86 years, with the most common age group being 25–45 years. Fever was the most frequently reported symptom (38%), and 51.5% of the patients had a documented history of contact with livestock. Cultures prepared from the collected samples identified 44 positive cases using standard biochemical methods. Then, the samples were evaluated using the Wright test, 2-mercaptoethanol, ELISA, PCR for the omp31 gene, and real-time PCR for the bcsp31 gene. All isolated strains were identified as Brucella melitensis. In this study, the real-time PCR (targeting bcsp31) showed the highest sensitivity (77.27%), followed by the Wright test (75%) and PCR (omp31 gene) (72.72%). Culture with 58.1% had the lowest diagnostic sensitivity. Out of the total of 33 positive samples reported with the Wright test, 24 samples (72.72%) showed positive results in the 2 ME test. Using the ELISA method, IgG was detected in 22 samples (47.82%), and IgM was detected in 14 samples (30.43%) out of 44 investigated samples. The results of the present study highlight the necessity of using multiple diagnostic techniques to diagnose human brucellosis in addition to the clinical symptoms of suspected patients. In addition, the bcsp31 real-time PCR assay showed superior sensitivity compared to other methods. Given that serological methods still serve as the primary tools for estimating brucellosis prevalence, incorporating real-time PCR assays may help overcome the limitations of conventional diagnostics.
Introduction Male voiding dysfunction assessment is often challenging due to variability in clinical interpretation and limited standardized guidance for uroflowmetry evaluation. Objective This paper aims to develop an interpretable machine learning framework that integrates uroflow pattern recognition and feature analysis to support clinical assessment of male voiding function. Methods Based on 988 male records from a medical center, this paper proposes a multilayer feature verification framework combining statistical analysis and interpretable machine learning to identify key uroflowmetry parameters associated with clinician‐defined uroflow patterns. This framework enables cross‐validation of feature relevance to ensure robustness and clinical interpretability. Results First, the proposed machine learning model for automated uroflow pattern recognition achieves higher recognition quality in comparison with the compared methods. Second, the cross‐verification between statistical analysis and interpretable machine learning identifies maximum uroflow rate, average uroflow rate, and maximum flow time as the most important features. Third, the pattern‐specific descriptive distribution ranges were derived to facilitate interpretation of characteristic uroflow patterns. Conclusion Compared with prior studies focusing primarily on classification performance, the proposed framework enhances feature‐level interpretability and identifies the relevant uroflowmetry characteristics associated with pattern‐based assessment. An automated uroflow pattern recognition model is developed to support clinical interpretation of uroflowmetry findings in male patients. Overall, this framework emphasizes not only AI‐based recognition accuracy but also interpretable uroflow pattern characteristics, offering additional insights for uroflowmetry interpretation and future clinical evaluation.
After one year of meticulous preparation, our laboratory successfully obtained ISO 15189 accreditation. During this process, we accumulated valuable practical experience and comprehensively optimized the laboratory quality management system (QMS). Although this work adopts the structure of a research manuscript, it is primarily an experience summary report. This study aims to summarize the laboratory’s experience in preparing for ISO 15189 accreditation and identify practical lessons for other laboratories.
Objective Hyperthyroidism is a common endocrine disorder, yet its association with gout remains unclear. This study aimed to examine the association between hyperthyroidism and gout in patients aged 20–84 years using real‐world data. Methods We conducted a retrospective cohort study using the TriNetX federated health research network from 2006 to 2023. Patients aged 20–84 years were categorized into two groups: those with a diagnosis of hyperthyroidism (hyperthyroidism group) and those without (control group). Propensity score matching was performed based on demographics and comorbidities. A 1‐month lag period was applied to minimize detection bias. The primary outcome was newly diagnosed gout. Hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated using Cox proportional hazards models implemented in the TriNetX “Compare Outcomes” module. Results After propensity score matching and applying a 1‐month lag period, 82,051 patients in the hyperthyroidism group and 82,111 patients in the control group were included. During a 5‐year follow‐up period, 420 patients in the hyperthyroidism group and 227 patients in the control group developed gout, corresponding to crude event proportions of 0.512% and 0.276%, respectively. The Cox model revealed that hyperthyroidism was associated with a higher risk of gout (HR 1.890; 95% CI, 1.608–2.221). Conclusion Hyperthyroidism is associated with an increased risk of gout, highlighting the need for clinical awareness of this potential comorbidity.
Background Schizophrenia impacts around 23 million people worldwide. The pharmacotherapeutic response among people with schizophrenia varies significantly in terms of efficacy and adverse effects, with genetics being a major contributing factor. This review provides insights into the current understanding of how pharmacogenomics can optimize antipsychotic selection and dosage adjustments, leading to improved adherence and treatment outcomes. Methods PubMed and Google Scholar searches were thoroughly conducted to retrieve peer‐reviewed articles. All articles with information relevant to the aim of the review, published in English up to 2026, were reviewed and analyzed. A total of 41 articles were cross‐checked and included in the final report of this review. Results Pharmacogenomics can identify gene mutations in proteins such as HTR2C, which are associated with weight gain in patients taking antipsychotics. This allows clinicians to select antipsychotics with minimized side effects. Pharmacogenomics also facilitates in determining appropriate dosage, particularly for medications metabolized by polymorphic enzymes such as CYP2D6 and CYP2C19. Employing this knowledge in clinical settings helps in shared decision‐making between the physician and patient to increase drug safety, adherence, and reduce multiple therapeutic attempts and overall healthcare costs. Conclusion Despite the transformative potential of pharmacogenomics, its implementation faces several challenges, including limited patient diversity, data integration barriers, and clinical training gaps. Policymakers, researchers, and healthcare organizations should enhance collaboration to facilitate the integration of pharmacogenomics in the treatment of schizophrenia.
Background Polycystic ovary syndrome (PCOS) represents a prevalent endocrinopathy among women of reproductive age, characterized by ovulatory dysfunction and metabolic dysregulation. While oral contraceptives (OC) are first‐line therapy for PCOS‐related hyperandrogenism and menstrual irregularities, they demonstrate limited efficacy in improving metabolic parameters and restoring ovulation. This study evaluated whether combining time‐restricted eating (TRE) with OC enhances reproductive and metabolic outcomes. Methods In this 12‐week randomized trial, 140 women with PCOS (18–40 years) were assigned to OC with 8 h TRE (8 a.m.–4 p.m.) or OC alone. The primary outcome was ovulation rate and menstrual patterns, with secondary outcomes including weight, body mass index (BMI), waist circumference, fertility hormones, and metabolic factors. Data were analyzed via intention‐to‐treat with chi‐square tests, logistic regression, and ANCOVA‐adjusted models. Results In the OC + TRE group, post‐withdrawal ovulation (assessed after the 12‐week intervention and discontinuation of OC therapy) was significantly higher (70% vs. 48.6%, p = 0.010), especially in participants with insulin resistance (HOMA‐IR > 2.5). Reduced fasting insulin (Δ‐FINS < 0) correlated with increased ovulation likelihood (OR = 4.250; 95% CI: 1.368–13.202).OC + TRE yielded greater weight loss (−3.5 kg vs. −0.8 kg, p < 0.001), BMI, body fat, and waist circumference reductions versus OC alone. Menstrual regularity and metabolic syndrome prevalence did not differ between groups. Conclusion Combining TRE with OC therapy enhanced ovulation, particularly in insulin‐resistant participants, likely via reduced fasting insulin. This approach also achieved greater weight, BMI, body fat, and waist circumference reductions than OC alone, underscoring its potential for managing both reproductive and metabolic aspects of PCOS. Trial Registration: Chinese Clinical Trial Registry (ChiCTR): ChiCTR2300078263
The widespread use of immunotherapies and combination regimens in acute myeloid leukemia (AML) has made immune-related adverse events (irAEs), treatment-associated inflammatory toxicities, and the differentiation between infection and sterile inflammation major clinical challenges. However, molecular markers that capture biological states associated with inflammatory complications and treatment-related vulnerability remain lacking. Glutathione (GSH) metabolism plays a central role in redox homeostasis and detoxification, yet whether GSH-related transcriptional states are associated with prognosis and treatment-related inflammatory vulnerability in AML has not been systematically investigated. Here, transcriptomic and clinical data from GSE37642 were used as the training cohort, with external validation in GSE12417 and TCGA-LAML. GSH-related genes were curated from MSigDB, and a prognostic risk signature (RS_GSH) was constructed using univariable Cox regression, LASSO Cox regression, and bidirectional stepwise selection, yielding a five-gene signature (GSTA4, GSTO2, GSTT2, OPLAH, and PRDX6). This signature stratified AML patients into high- and low-risk groups with significantly different overall survival across training and validation cohorts. The high-risk group showed upregulation of GSH metabolism and detoxification pathways and enrichment for xenobiotic metabolism and was associated with adverse FAB subtypes and high-risk cytogenetic categories. Importantly, this high-risk phenotype may represent a GSH-centered detoxification and redox-stress background with potential relevance to treatment-related inflammatory toxicity, including irAEs and other treatment complications. Exploratory drug response prediction indicated potential differences in predicted sensitivity to navitoclax and piperlongumine. In conclusion, this GSH-related prognostic model supports prognostic assessment in AML and provides a biological framework for further investigating treatment-related inflammatory toxicity and irAEs, warranting prospective validation in clinically annotated cohorts.
Objective This study aimed to examine the current status of social anxiety and identify associated factors among young and middle‐aged adults with hearing loss (HL) to inform clinical interventions. Study Design A cross‐sectional study was conducted involving young and middle‐aged individuals diagnosed with HL. Setting Participants were recruited from the department of otolaryngology at a tertiary Grade A general hospital using convenience sampling. Methods A total of 218 hospitalized individuals with HL were included. Data were collected using a general information questionnaire, the abbreviated 6‐item Social Interaction Anxiety Scale (SIAS‐6), and the 6‐item Social Phobia Scale (SPS‐6). Results Of the 218 distributed questionnaires, 213 were valid. Among the respondents, 21.1% ( n = 45) had SIAS‐6 scores ≥ 12, and 25.8% ( n = 55) had SPS‐6 scores ≥ 9. Logistic regression analysis identified sleep disorders (OR = 2.716; 95% CI: 1.241–5.944), monthly individual incomes (OR = 0.451; 95% CI: 0.206–0.990), and higher depression levels (OR = 8.041; 95% CI: 3.700–17.476) as significant predictors of social interaction anxiety ( p < 0.05). Lower monthly individual incomes (OR = 0.388; 95% CI: 0.188–0.799) and higher depression levels (OR = 9.080; 95% CI: 4.436–18.584) were significantly associated with social phobia ( p < 0.05). Conclusion Social interaction anxiety and social phobia are relatively common among young and middle‐aged adults with HL. Medical staff should assess social anxiety in this population, implement timely targeted interventions, and prevent or reduce the occurrence of social interaction anxiety and social phobia in young and middle‐aged patients with HL.
Background Sickle cell disease (SCD) is associated with progressive renal complications collectively termed sickle cell nephropathy, which significantly contribute to morbidity and mortality. However, the burden and distribution of renal manifestations across disease stages remain inconsistently reported. Objective This study aimed to systematically review and meta‐analyze the prevalence of renal involvement in individuals with SCD, including albuminuria (reported as microalbuminuria and macroalbuminuria in some studies), proteinuria, and chronic kidney disease (CKD) and to explore genotype‐ and age‐group‐specific differences. Methods A systematic review and meta‐analysis were conducted in accordance with PRISMA guidelines. PubMed, Scopus, and Google Scholar were searched from 2015 to 2026. Observational studies reporting prevalence data on renal outcomes in SCD were included. Pooled prevalence estimates were calculated using a random‐effects model with Freeman–Tukey transformation. Heterogeneity was assessed using the I 2 statistic, and subgroup analyses were performed based on genotype and age group. Results A total of thirty‐eight (38) studies were included. The pooled prevalence of albuminuria was 34% (95% CI: 27%–41%; I 2 = 93.2%). Among hemoglobin SS (HbSS) patients, albuminuria prevalence was higher at 39% (95% CI: 29%–49%; I 2 = 93.9%) and 22% (95% CI: 1%–56%; I 2 = 68.7%) among hemoglobin SC (HbSC) patients. The pooled prevalence of CKD was 43% (95% CI: 26%–61%; I 2 = 99.2%), increasing to 65% (95% CI: 35%–89%; I 2 = 96.8%) in HbSS individuals. Microalbuminuria was the most frequently reported manifestation (29%), followed by proteinuria (18%) and macroalbuminuria (11%). Age‐stratified analysis showed lower albuminuria prevalence in children (23%) compared to adults (48%), suggesting an increasing burden of renal abnormalities with age. Conclusion Renal complications are highly prevalent in SCD, with albuminuria representing an early marker of kidney injury and CKD reflecting advanced disease. The higher burden observed in HbSS patients and adults underscores the progressive nature of sickle cell nephropathy. Early detection and routine monitoring are essential to mitigate progression and improve long‐term outcomes.
Objective Primary squamous cell carcinoma of the thyroid (PSCCT) represents a rare, aggressive malignancy with insufficiently characterized molecular underpinnings and a lack of robust prognostic instruments. This investigation aimed to derive novel prognostic biomarkers and to develop a composite risk‐stratification model by fusing multiomics data with standard clinical parameters through advanced machine learning methodologies. Methods We performed a retrospective analysis on a cohort of 250 PSCCT patients sourced from the SEER registry (1975–2018). Clinically relevant variables—encompassing age, sex, tumor grade, SEER stage, and treatment received—were merged with transcriptome‐imputed biomarkers identified via artificial intelligence (AI)–powered bioinformatics analysis. A machine learning–enhanced Cox proportional hazards model was implemented to ascertain determinants of overall survival (OS) and disease‐specific survival (DSS). The interpretability of the model was increased through SHapley Additive exPlanations (SHAP) to quantify and rank feature importance. Results Univariate evaluation indicated significant associations of tumor grade, extrathyroidal extension, SEER stage, and surgical approach with both OS and DSS (all p values < 0.05). Multivariate Cox regression analysis validated tumor grade and SEER stage as independent prognostic variables for these survival outcomes. Leveraging machine learning for feature selection, we subsequently delineated a suite of transcriptomic biomarkers that augment traditional clinical predictors. The final AI‐integrated prognostic model showed improved accuracy over established staging systems in forecasting 3‐year survival (concordance index: 0.79 compared to 0.68) and efficiently segregated patients into discrete risk categories. Conclusion We have established and validated an interpretable, machine learning–based prognostic framework for PSCCT that synergistically integrates clinical and multiomics biomarkers. This methodology advances individualized outcome forecasting and can inform clinical decision‐making for this uncommon but lethal cancer. While tumor grade endures as a pivotal prognostic factor, AI‐refined biomarker panels provide a promising strategy to optimize risk assessment and personalize therapeutic interventions in PSCCT.
Background Heat stroke (HS) is a life‐threatening condition, and its prognosis is severely compromised when accompanied by cardiorenal syndrome (CRS). Early identification of HS‐induced CRS is essential for enhancing clinical outcomes. Objective To develop and validate a nomogram for early prediction of HS‐induced CRS. Methods This retrospective cohort study involved 277 HS patients from eight hospitals in Sichuan Province, China. Key clinical demographics, vital signs, and routine laboratory parameters on admission were systematically analyzed. Independent predictors of HS‐induced CRS were identified using multivariate logistic regression, and a nomogram was constructed to facilitate early prediction of this life‐threatening complication. Results In this study of 277 HS patients, 68 developed CRS, with an incidence rate as high as 24.5%. Among the non‐CRS group, 77 died during hospitalization, compared to 54 patients in the CRS group, representing a significant difference (36.8% vs. 79.4%, p < 0.001). The CRS group faced a 2.2‐fold higher mortality risk (risk ratio [RR] = 2.16, 95% CI: 1.78–2.62) and a 6.6‐fold increase in the odds of death compared to non‐CRS patients (odds ratio [OR] = 6.61, 95% CI: 3.45–12.68). Independent predictors of HS‐induced CRS were identified as HR, ANC, DB, Mb, and INR on admission. The nomogram exhibited strong predictive accuracy, with an area under the curve (AUC) of 0.813 (95% CI, 0.76–0.87). Both the calibration curve and clinical decision curve analysis (DCA) confirmed the model’s strong predictive performance. Conclusion The developed nomogram effectively predicts the risk of HS‐induced CRS using accessible clinical indicators (INR, ANC, HR, DB, and Mb). This approach is crucial for enhancing patient outcomes and optimizing the utilization of emergency medical resources, particularly in regions with high HS incidence and limited healthcare capacities, though future prospective validation is warranted.
Background Thalassaemia patients are experiencing neurological complications as survival improves with advanced treatment. However, thalassaemia‐associated peripheral neuropathy (ThalPN) and cognitive impairment (CogImp) prevalence and clinical determinants among adult thalassaemia patients remain unknown. Methods This cross‐sectional study included adult thalassaemia patients from two major Malaysian centres. CogImp psychiatric patients were excluded. The validated neuropathy symptom score (NSS) and neuropathy disability score (NDS) were used to measure ThalPN, with a total neuroscore (TNS) of ≥ 8 indicating ThalPN. Patients who agreed to a nerve conduction study (NCS) were tested. Cognitive function was assessed using the Malay Montreal Cognitive Assessment (MoCA‐M), with education‐adjusted scores of < 22 indicating CogImp. Health‐related quality of life (HRQOL) was measured using EQ‐5D‐3L. The relationship with risk factors was analysed using logistic regression. Results The cohort comprised 118 transfusion‐dependent thalassaemia and 59 non‐transfusion‐dependent thalassaemia patients with a median age of 30 years (range: 18–66 years). ThalPN prevalence was 24.9%, mostly subclinical (17.5%). Of the 21 NCS patients, only three exhibited abnormal results. CogImp prevalence was 11.9%. Older age independently predicted higher neuropathy scores ( β = 0.082, 95% CI 0.022–0.142, p = 0.008). Diabetes mellitus ( β = −8.07, p = 0.010) and splenectomy ( β = −1.43, p = 0.022) independently predicted worse cognitive scores. Ferritin levels and transfusion dependence were not significantly associated with ThalPN or CogImp. ThalPN severity categories progressively worsened HRQOL ( p = 0.015). A significant discrepancy existed between patient‐reported symptoms in NSS versus objective clinical indicators, NDS. Conclusions The neurological burden among thalassaemia patients is profound, and the discrepancy between indicators and symptoms suggests small fibre involvement. These findings support the recommendation of neurological screening with thalassaemia treatment. Multicentre, longitudinal studies are needed to evaluate neurological development and validate findings across patient groups.
