Abstract In the current bioinformatics landscape, where R-centric and Python-centric ecosystems coexist and overlap, there is an increasing demand for the organic integration of these disparate development environments. As bioinformatics practices become ubiquitous, it is crucial to lower the technical barriers for biologists to adopt software engineering standards—such as version control, environment reproducibility, code readability, continuous integration/continuous deployment (CI/CD), and comprehensive documentation—which often present significant implementation hurdles for biologists with limited programming experience. To address these challenges, we developed BasalCell ( https://github.com/yo-aka-gene/BasalCell ), a project scaffolding system designed to provide a standardized, easily reproducible template that integrates these essential features by default—enabling researchers to seamlessly manage multi-language environments and automate rigorous development workflows, ultimately fostering greater transparency and reliability in biological data science.

Background: Anemia is a common systemic condition associated with adverse maternal, perioperative, and cardiovascular outcomes. Although timely screening is clinically important, diagnosis still relies on invasive blood testing. Palpebral conjunctival pallor has traditionally been used as a noninvasive indicator of anemia, but its diagnostic accuracy remains limited. This study aimed to develop and validate a deep learning system to estimate hemoglobin (Hb) concentration and screen for anemia using palpebral conjunctiva images captured with a smartphone-compatible slit-lamp microscope. Methods: In this prospective observational study, 225 Japanese participants (20-92 years) underwent conjunctival imaging and blood testing. Palpebral conjunctiva videos were obtained using the Smart Eye Camera. Video frames were processed using automated anterior-segment segmentation and conjunctiva extraction. A ConvNeXt-based regression model was trained to predict Hb values. Anemia was defined using sex-specific Hb thresholds. Results: From 225 videos, 53,776 frames were extracted, yielding 9903 quality-filtered conjunctiva images (training: 8082; test: 1821). Video-level predicted Hb values moderately correlated with measured Hb (r = 0.42). For anemia screening, frame-level analysis achieved an AUC of 0.75, with accuracy of 0.76, sensitivity of 0.71, and specificity of 0.79. Video-level aggregation achieved 69% accuracy. Conclusions: Deep learning analysis of palpebral conjunctiva images acquired with a portable slit-lamp microscope demonstrated the feasibility of non-invasive hemoglobin estimation and anemia screening. Although the proposed approach achieved moderate performance, further improvements in model accuracy and prospective multi-center validation are required before clinical implementation.

Purpose:To predict the risk of diabetic macular edema (DME) onset and to identify features of the risk subgroups. Design:Population-based observational study with a case-control design. Subjects and Controls:Health checkup and diagnosis data from the JMDC claims database (January 2005-July 2020), one of the largest Japanese epidemiological databases, were used. From 272 337 individuals diagnosed with type 2 diabetes (International Classification of Diseases, 10th Revision E11), we analyzed 2368 pairs of DME and non-DME individuals, which matched 1:1 by the balancing score calculated from regression analysis of DME with sex, age, months of observation, number of checkups, duration of diabetes, and months until the first checkup. Methods:We employed a multivariate Cox proportional hazards model, regularized Cox models, and a random survival forest (RSF). These models were trained via ID-level bootstrap resampling using 43 health checkup variables (missing ratio <50%) and 404 high-incidence diseases within 6 months, along with age, sex, and duration of diabetes mellitus, to assess DME risk. Temporal changes in RSF-predicted risk scores were analyzed using nonlinear modeling techniques. Main Outcome Measures:Concordance index (C-index), integrated Brier score (IBS), and cumulative/dynamic mean area under the receiver operating characteristic curve (AUC). Results:Thirteen checkup items and 44 disease history variables were significantly associated with the onset of DME. The RSF identified 43.8% of DME cases >5 years prior to onset, with a specificity of 85.5%. The RSF achieved median C-index, IBS, and mean AUC values of 0.694 (95% confidence interval, 0.688-0.697), 0.181 (0.179-0.184), and 0.750 (0.739-0.756), respectively, outperforming both multivariate and univariate Cox models. Three distinct DME risk subgroups were suggested by temporal changes in the RSF-predicted risk score. Predictors of DME onset varied markedly among these subgroups. In the explicit high-risk subgroup, urinary protein and urinary sugar were highly important, and liver function-related blood tests, such as alanine transaminase and γ-gultamyltransferase, were also ranked high in variable importance metrics. Anemia-related laboratory tests were associated with DME development only in this subgroup. Conclusions:Random survival forest demonstrated superior performance in relative risk prediction of DME using health checkup data. External validation remains an essential prerequisite before any clinical application. Financial Disclosures:Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Routine echocardiography at one month after diagnosis is the current standard for screening coronary artery abnormalities (CAA), a major complication of Kawasaki disease. The study aimed to develop and validate models to predict CAA and assess whether routine echocardiography could be safely reduced in low-risk patients. Two prospective multicenter Japanese registries were utilized: PEACOCK (development/internal validation) and Post-RAISE (external validation). Variables obtained within one week of diagnosis were used to predict CAA at one month after diagnosis, defined as a maximum coronary artery Z score (Zmax) ≥ 2. The models included simple models using the previous maximum Z score only, logistic regression models, and machine learning models (LightGBM and XGBoost). Discrimination, calibration, and clinical utility were assessed. Among 4,973 PEACOCK and 2,438 Post-RAISE patients, the CAA incidence was 5.5

BackgroundThe stay-at-home orders, lockdowns, and states of emergency of the Coronavirus Infectious Disease emerged in 2019 (COVID-19) pandemic have affected the mental health of school-aged children. Previous reports of psychological distress in adolescents during the pandemic have been mixed, however, with some reports showing increases in psychological distress and others suggesting decreases. To accurately assess the impact of the pandemic, we need to be able to compare psychological assessments longitudinally, both before and during the pandemic. However, current statistical methods have limitations for reconstructing the complex trajectory of psychological states as captured by short-item questionnaires.Methods and findingsIn this study, we analyzed monthly Kessler 6-item Psychological Distress Scale (K6) questionnaire responses collected from 16- to 18-year-old high school students participating in the population-neuroscience Tokyo TEEN Cohort (pn-TTC) in Japan (1,278 responses from 84 participants). Participants included 42 males and 42 females. The pn-TTC is a population-based longitudinal study conducted in Tokyo, Japan that follows children to investigate their developmental and mental health trajectories. In addition to conventional statistical approaches that summarize multiple questionnaire items into a composite score, we applied "energy landscape analysis," a method derived from statistical physics that models multivariate psychological states as a dynamic system of interactions among K6 questionnaire items, to visualize longitudinal changes in psychological distress before and during the COVID-19 pandemic (July 2019 to September 2021). Here, we define the depressive and healthy states as configurations in which all six K6 items are above or below each participant's individual mean, respectively. Before the pandemic, the healthy state occurred 11.0 times as frequently as the depressive state. In contrast, during the pandemic, the relative frequency of the healthy state increased to 18.2, 18.5, and 15.0 times that of the depressive state, respectively. The evolving energy landscape revealed an association between the pandemic period and a lower likelihood of being in a depressive state. We also identified two groups of students with different K6 dynamics and energy landscapes. The first group consisted of 61 participants whose total K6 score was relatively low (less than 5) and stable over time, and the second group consisted of 23 participants whose total K6 score was higher (with most being higher than 5) and less stable. The latter group showed a greater change in cortical thickness in the caudal part of the middle frontal gyrus (cMFG) (t-statistic = -2.36, p-value = 0.019, q-value = 0.048) and the temporal pole (TP) (t = 3.08, p = 0.0023, q = 0.012), as measured by magnetic resonance imaging, in the direction of accelerated adolescent brain development. Because all participants lived in Tokyo, generalizability remains limited, and as the association between psychological states and brain development is descriptive, future studies in diverse cohorts are needed to examine causality.ConclusionsBy revealing associations between the COVID-19 pandemic and lower levels of psychological distress and healthier mental health states, our work demonstrates the potential of using dynamical systems theory, such as the energy landscape analysis, to interpret health and disease metrics in psychology and psychiatry. This approach may improve mental health surveillance for the next pandemic.

Modern clinical oncology faces an unprecedented data complexity that exceeds human analytical capacity, making artificial intelligence (AI) integration essential rather than optional. This review examines the dual impact of AI on productivity enhancement and creative discovery in cancer care. We trace the evolution from traditional machine learning to deep learning and transformer-based foundation models, analyzing their clinical applications. AI enhances productivity by automating diagnostic tasks, streamlining documentation, and accelerating research workflows across imaging modalities and clinical data processing. More importantly, AI enables creative discovery by integrating multimodal data to identify computational biomarkers, performing unsupervised phenotyping to reveal hidden patient subgroups, and accelerating drug development. Finally, we introduce the FUTURE-AI framework, outlining the essential requirements for translating AI models into clinical practice. This ensures the responsible deployment of AI, which augments rather than replaces clinical judgment, while maintaining patient-centered care.

Design of experiments (DOE) principles are increasingly applied to biological assays, yet it remains unclear whether their foundational assumption, orthogonal decomposition, holds in nonlinear biological systems. We addressed this question using Perturb-seq as a case study. By benchmarking a design commonly used in Perturb-seq and related experiments against the orthogonal Plackett-Burman (PB) design via simulations, we uncovered a counter-intuitive phenomenon: while orthogonal designs generally excel, the multicollinearity inherent to the common design is functionally advantageous in systems with significant signal amplification. This challenges the blind application of DOE to biology. Based on these findings, we developed the PB suitability index (PBSI), a simple, parameter-free metric that predicts the optimal design solely from network structure. Our work not only provides practical guidelines for Perturb-seq but also establishes a "biology-oriented DOE" framework, bridging the gap between statistical rigor and biological complexity. ### Competing Interest Statement The authors have declared no competing interest. Japan Society for the Pro- motion of Science (JSPS) KAKENHI, JP25K21344

It is important to explore not only what is lost with the progression of dementia, but also what is left and what can be compensated or developed by adapting to the special needs of the conditions. Some studies suggest that as dementia progresses, people become less emotionally expressive and have difficulties in distinguishing between other people's facial expressions. On the other hand, it is also said as cognitive function declines, anger and anxiety increase, placing a burden on caregivers. Here we report the richness of emotions in people with dementia expressed in their interviews. We analysed the transcripts of 88 interviews of people with dementia (4.8 years after diagnosis), which lasted about 60-90 min, sometimes even longer. Those transcripts are valuable, as they show that people with dementia can express themselves verbally for a long time. From the analysis of the transcripts using Paul Ekman's basic emotions, we found that the strongest emotion expressed in the interviews was happiness. We also conducted a survey of the general public recruited through X about the emotions of people with dementia, and found that people without dementia imagined that the strongest emotion felt by people with dementia was fear. Our results suggest that the cognitive abilities and emotional states of people with dementia can be more robust than people without dementia typically perceive. It might be possible to augment happiness-related emotions by the support of those around people with dementia, and the cognitive efforts of the affected people themselves.

Hypoglycemia and hyperglycemia are common complications among critically ill patients. Maintaining blood glucose levels in the normal range is crucial but challenging due to complex influencing factors. This study aimed to develop a machine learning model that predicts hypo- or hyperglycemia 6 h in advance in patients admitted to the intensive care unit (ICU). We analyzed electronic health records of 8,853 ICU patients (1,350,097 records) from a single center in Japan (2010-2022). Hypoglycemia and hyperglycemia were defined as blood glucose levels ≤ 80 mg/dL (4.4 mmol/L) and ≥ 180 mg/dL (10 mmol/L), respectively. We developed prediction models using routinely collected ICU data, including demographic, physiological, laboratory, and treatment variables. Machine learning models were developed using eXtreme Gradient Boosting (XGBoost), random forest, neural networks, and logistic regression. The XGBoost model demonstrated the highest performance with an area under the curve (AUC) of 0.939 and an F1 score of 0.520 for predicting hypoglycemia and an AUC of 0.919 and an F1 score of 0.702 for predicting hyperglycemia. It also achieved high calibration and net benefit. The machine learning models, notably the XGBoost algorithm, accurately predicted glucose abnormalities in critically ill ICU patients. These findings support its potential as a tool for early detection and proactive management of dysglycemia in critical care.

Background The early identification of developmental concerns requires understanding individual differences that may represent early signs of neurodevelopmental conditions. However, few studies have longitudinally examined how child and maternal factors interact to shape these early developmental characteristics. Objective We aim to identify factors from the perinatal to infant periods associated with early developmental characteristics that may precede formal diagnoses and propose a method for evaluating individual differences in neurodevelopmental trajectories. Methods A prospective longitudinal observational study of 147 mother-child pairs was conducted from gestation to 12 months post partum. Assessments included prenatal questionnaires and blood collection, cord blood at delivery, and postpartum questionnaires at 1, 6, and 12 months. The Modified Checklist for Autism in Toddlers (M-CHAT) was used to evaluate developmental characteristics that might indicate early signs of atypical neurodevelopment. Polychoric or polyserial correlation coefficients assessed relationships between M-CHAT scores and longitudinal variables. L2-regularized logistic regression and Shapley Additive Explanations predicted M-CHAT scores and determined feature contributions. Results Twenty-one factors (4 prenatal, 3 at birth, and 14 postnatal) showed significant associations with M-CHAT scores (adjusted P values<.05). The predictive accuracy for M-CHAT scores demonstrated reasonable predictive accuracy (area under the receiver operating characteristic curve=0.79). Key predictors included infant sleep status after 6 months (nighttime sleep duration, bedtime, and difficulties falling asleep), maternal Kessler Psychological Distress Scale scores, and Mother-to-Infant Bonding Scale scores after late gestation. Conclusion Maternal psychological distress, mother-infant bonding, and infant sleep patterns were identified as significant predictors of early developmental characteristics that may indicate emerging developmental concerns. This study advances our understanding of early developmental assessment by providing a novel approach to identifying and evaluating early indicators of atypical neurodevelopment.

Objective:Complications in neuroendovascular therapy for cerebral aneurysm (AN) affect the clinical course of patients. Patient conditions, operating procedures, and operator expertise were highlighted as risk factors for complications. These risk factors often combine and constitute particularly strong risks, resulting in a worsened clinical course. In this study, we performed a multifactorial assessment of complication risks in neuroendovascular therapy. Methods:We analyzed patient data from the Japanese Registry of NeuroEndovascular Therapy 3, which is a nationwide retrospective cohort study of neuroendovascular procedures conducted between 2010 and 2014. Patients who underwent coil embolization for a ruptured anterior communicating artery (Acom) AN, an internal carotid artery-posterior communicating artery (IC-PC) AN, or basilar artery bifurcation (BA-bif) AN were included in this analysis. Information on 16 explanatory variables and 1 objective variable for each patient was obtained from the dataset as nominal variables. The explanatory variables consisted of patient factors, procedural factors, and an operator factor. The objective variable was whether the following complications occurred: intraprocedural bleeding, postprocedural bleeding, and procedure-related infarction. The specific situations involving multiple risk factors associated with high complication rates were identified using a programmed method. The impact of the absence of a supervising physician was also assessed. Results:A total of 2971 patients were analyzed. The complication rates for patients with Acom ANs, IC-PC ANs, and BA-bif ANs were 12.9%, 10.2%, and 13.7%, respectively. A total of 15 specific situations were identified as follows: 3 related to an Acom AN, with complication rates ranging from 19.3% to 20.3%; 4 related to an IC-PC AN, with complication rates ranging from 15.6% to 17.9%; and 8 related to a BA-bif AN, with complication rates ranging from 20.6% to 33.3%. In 4 of these situations, the absence of a supervising physician significantly impacted complication rates. For instance, the complication rate for patients with IC-PC AN treated under local anesthesia was 16.0% overall, but it was 23.8% without supervising physicians. Conclusion:Multifactorial assessment based on patient, procedural, and operator factors provides more reliable risk estimations and will help improve the clinical course.

Regular and sufficient sleep is essential for children's development. Sleep deficiency is related to various conditions, such as autism spectrum disorder and attention-deficit hyperactivity disorder. Parent-monitored sleep logs are widely used, but cannot provide comprehensive assessment of paediatric sleep rhythms, because each child's developmental stage, diurnal variations, and daily fluctuations need to be considered. To address this limitation,we developed an artificial intelligence (AI) -based approach that combines unsupervised machine-learning techniques to analyze pediatric sleep data. This approach allowed us to analyse various sleep features comprehensively and visualize typical sleep development. To illustrate the application of our approach, we conducted an association analysis with developmental disorders. We identified specific characteristics of children with developmental disorders. We also introduced a new metric, the degree of deviation from typical development, which provided a more accurate identification of their sleep patterns than achieved with traditional metrics. Our AI-based method may be useful for identifying abnormal sleep rhythm features that could be associated with developmental disorders using practical parent-reported data.

Background The early identification of developmental concerns requires understanding individual differences that may represent early signs of neurodevelopmental conditions. However, few studies have longitudinally examined how child and maternal factors interact to shape these early developmental characteristics. Objective We aim to identify factors from the perinatal to infant periods associated with early developmental characteristics that may precede formal diagnoses and propose a method for evaluating individual differences in neurodevelopmental trajectories. Methods A prospective longitudinal observational study of 147 mother-child pairs was conducted from gestation to 12 months post partum. Assessments included prenatal questionnaires and blood collection, cord blood at delivery, and postpartum questionnaires at 1, 6, and 12 months. The Modified Checklist for Autism in Toddlers (M-CHAT) was used to evaluate developmental characteristics that might indicate early signs of atypical neurodevelopment. Polychoric or polyserial correlation coefficients assessed relationships between M-CHAT scores and longitudinal variables. L2-regularized logistic regression and Shapley Additive Explanations predicted M-CHAT scores and determined feature contributions. Results Twenty-one factors (4 prenatal, 3 at birth, and 14 postnatal) showed significant associations with M-CHAT scores (adjusted P values<.05). The predictive accuracy for M-CHAT scores demonstrated reasonable predictive accuracy (area under the receiver operating characteristic curve=0.79). Key predictors included infant sleep status after 6 months (nighttime sleep duration, bedtime, and difficulties falling asleep), maternal Kessler Psychological Distress Scale scores, and Mother-to-Infant Bonding Scale scores after late gestation. Conclusion Maternal psychological distress, mother-infant bonding, and infant sleep patterns were identified as significant predictors of early developmental characteristics that may indicate emerging developmental concerns. This study advances our understanding of early developmental assessment by providing a novel approach to identifying and evaluating early indicators of atypical neurodevelopment.

Background:Patients' clinical status often evolves rapidly after an initial diagnosis, with each patient exhibiting a distinct disease trajectory. As a result, static risk scores fall short in supporting timely interventions-an issue highlighted by COVID-19, where deaths have stemmed from heterogeneous pathways such as pneumonia, multiorgan failure, or exacerbation of preexisting conditions. Objective:This study aims to propose a dynamic prognostic risk assessment framework based on longitudinal data collected during hospitalization, using COVID-19 as an example. Our aim was to develop and validate an interpretable framework that (1) screens prognosis at admission and (2) dynamically updates mortality risk throughout hospitalization, thereby providing clinicians with early, explainable warnings while minimizing additional cognitive load. Methods:In this retrospective study, we extracted electronic medical records of 382 COVID-19 cases treated at Tokyo Shinagawa Hospital between January 27 and September 30, 2020. At admission, gradient boosting decision trees (Light Gradient Boosting Machine) were used to predict the maximum clinical deterioration, including death, based on data available at initial diagnosis. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC). For in-hospital monitoring, random survival forests (RSF) were trained on a longitudinal dataset that combined static demographic characteristics with serially measured vital signs and laboratory results. The model dynamically assessed daily mortality risk by calculating a 7-day cumulative hazard function, with risk scores recalculated each day during hospitalization. RSF accuracy was evaluated in an independent one-third test set using the concordance index (C-index), an integrated Brier score (1-50 days), and mean time-dependent AUC. SurvSHAP(t), an extension of Shapley Additive Explanations, was applied to provide time-dependent explanations of each variable's contribution to the prediction. Results:The prediction at initial diagnosis showed good agreement with the actual severity outcomes (AUC of 0.717 for predicting hospitalization/severity ≥2; 0.878 for severity ≥3; 0.951 for severity ≥4; 0.952 for severity ≥5; and 0.970 for death/severity=6), although some cases exhibited discrepancies between the predicted and actual prognoses. The dynamic mortality risk assessment during hospitalization using the RSF achieved a test-set C-index of 0.941, an integrated Brier score of 0.315, and a mean time-dependent AUC of 0.936. This dynamic assessment was able to distinguish between dead and surviving patients as early as 1-2 weeks before the outcome. Early in hospitalization, C-reactive protein was an important risk factor for mortality; during the middle period, peripheral oxygen saturation (SpO2) gained importance; and immediately before death, platelets and β-D-glucan were the primary risk factors. Conclusions:Integrating static admission triage with daily, explainable RSF predictions enables early identification of patients with COVID-19 at high risk of deterioration. By surfacing phase-specific, actionable predictors, the framework supports timely interventions and more efficient resource allocation. Prospective, multicenter studies are warranted to validate its generalizability and clinical impact.

BackgroundOne life event that requires extensive resilience and adaptation is parenting. However, resilience and perceived support in child-rearing vary, making the real-world situation unclear, even with postpartum checkups. ObjectiveThis study aimed to explore the psychosocial status of mothers during the child-rearing period from newborn to toddler, with a classifier based on data on the resilience and adaptation characteristics of mothers with newborns. MethodsA web-based cross-sectional survey was conducted. Mothers with newborns aged approximately 1 month (newborn cohort) were analyzed to construct an explainable machine learning classifier to stratify parenting-related resilience and adaptation characteristics and identify vulnerable populations. Explainable k-means clustering was used because of its high explanatory power and applicability. The classifier was applied to mothers with infants aged 2 months to 1 year (infant cohort) and mothers with toddlers aged >1 year to 2 years (toddler cohort). Psychosocial status, including depressed mood assessed by the Edinburgh Postnatal Depression Scale (EPDS), bonding assessed by the Postpartum Bonding Questionnaire (PBQ), and sleep quality assessed by the Pittsburgh Sleep Quality Index (PSQI) between the classified groups, was compared. ResultsA total of 1559 participants completed the survey. They were split into 3 cohorts, comprising populations of various characteristics, including parenting difficulties and psychosocial measures. The classifier, which stratified participants into 5 groups, was generated from the self-reported scores of resilience and adaptation in the newborn cohort (n=310). The classifier identified that the group with the greatest difficulties in resilience and adaptation to a child’s temperament and perceived support had higher incidences of problems with depressed mood (relative prevalence [RP] 5.87, 95% CI 2.77-12.45), bonding (RP 5.38, 95% CI 2.53-11.45), and sleep quality (RP 1.70, 95% CI 1.20-2.40) compared to the group with no difficulties in perceived support. In the infant cohort (n=619) and toddler cohort (n=461), the stratified group with the greatest difficulties had higher incidences of problems with depressed mood (RP 9.05, 95% CI 4.36-18.80 and RP 4.63, 95% CI 2.38-9.02, respectively), bonding (RP 1.63, 95% CI 1.29-2.06 and RP 3.19, 95% CI 2.03-5.01, respectively), and sleep quality (RP 8.09, 95% CI 4.62-16.37 and RP 1.72, 95% CI 1.23-2.42, respectively) compared to the group with no difficulties. ConclusionsThe classifier, based on a combination of resilience and adaptation to the child’s temperament and perceived support, was able identify psychosocial vulnerable groups in the newborn cohort, the start-up stage of childcare. Psychosocially vulnerable groups were also identified in qualitatively different infant and toddler cohorts, depending on their classifier. The vulnerable group identified in the infant cohort showed particularly high RP for depressed mood and poor sleep quality.