Semiconductive Behavior and Photoconductivity of Uranyl Dithiophosphinate Single Crystal.

Herein, we detail the synthesis, structure, and photoconductivity of the uranyl dithiophosphinate single crystal UO2[S2P(C6H5)2]2(CH3OH)·CH3OH (denoted as U-DPDPP). The formation of bonds between uranyl ions and sulfur-based ligands endows U-DPDPP with a distinct electronic absorption property with a broadband spectrum spanning from 250 to 550 nm, giving rise to a unique semiconductive property. Under X-ray illumination, U-DPDPP displays a distinctive photoconductivity response, with a charge carrier mobility lifetime (µτ) of 2.78 × 10-4 cm2·V-1 achieved, which contradicts the electronic-silence behavior of uranyl nitrate crystal.

Farnesoid X receptor promotes non-small cell lung cancer metastasis by activating Jak2/STAT3 signaling via transactivation of IL-6ST and IL-6 genes.

Metastasis accounts for the majority of cases of cancer recurrence and death in patients with advanced non-small cell lung cancer (NSCLC). Farnesoid X Receptor (FXR) is a bile acid nuclear receptor that was recently found to be upregulated in NSCLC tissues. However, whether and how FXR regulates NSCLC metastasis remains unclear. In the present study, it was found that FXR promoted the migration, invasion, and angiogenic ability of NSCLC cells in vitro, and increased NSCLC metastasis in a mouse model in vivo. Mechanistic investigation demonstrated that FXR specifically bound to the promoters of IL-6ST and IL-6 genes to upregulate their transcription, thereby leading to activation of the Jak2/STAT3 signaling pathway, which facilitated tumor migration, invasion, and angiogenesis in NSCLC. Notably, Z-guggulsterone, a natural FXR inhibitor, significantly reduced FXRhigh NSCLC metastasis, and decreased the expression of FXR, IL-6, IL-6ST, and p-STAT3 in the mouse model. Clinical analysis verified that FXR was positively correlated with IL-6, IL-6ST and p-STAT3 expression in NSCLC patients, and was indicative of a poor prognosis. Collectively, these results highlight a novel FXR-induced IL-6/IL-6ST/Jak2/STAT3 axis in NSCLC metastasis, and a promising therapeutic means for treating FXRhigh metastatic NSCLC.

Identification of ipsilateral supraclavicular lymph node metastasis in breast cancer based on LASSO regression with a high penalty factor.

Aiming at the problems of small sample size and large feature dimension in the identification of ipsilateral supraclavicular lymph node metastasis status in breast cancer using ultrasound radiomics, an optimized feature combination search algorithm is proposed to construct linear classification models with high interpretability. The genetic algorithm (GA) is used to search for feature combinations within the feature subspace using least absolute shrinkage and selection operator (LASSO) regression. The search is optimized by applying a high penalty to the L1 norm of LASSO to retain excellent features in the crossover operation of the GA. The experimental results show that the linear model constructed using this method outperforms those using the conventional LASSO regression and standard GA. Therefore, this method can be used to build linear models with higher classification performance and more robustness.

Childhood maltreatment and risk of endocrine diseases: an exploration of mediating pathways using sequential mediation analysis.

BACKGROUND: Adverse childhood experiences (ACEs), including childhood maltreatment, have been linked with increased risk of diabetes and obesity during adulthood. A comprehensive assessment on the associations between childhood maltreatment and all major endocrine diseases, as well as the relative importance of different proposed mechanistic pathways on these associations, is currently lacking. METHODS: Based on the UK Biobank, we constructed a cohort including 151,659 participants with self-reported data on childhood maltreatment who were 30 years of age or older on/after January 1, 1985. All participants were followed from the index date (i.e., January 1, 1985, or their 30th birthday, whichever came later) until the first diagnosis of any or specific (12 individual diagnoses and 9 subtypes) endocrine diseases, death, or the end of follow-up (December 31, 2019), whichever occurred first. We used Cox models to examine the association of childhood maltreatment, treated as continuous (i.e., the cumulative number of experienced childhood maltreatment), ordinal (i.e., 0, 1 and ≥ 2), or binary (< 2 and ≥ 2) variable, with any and specific endocrine diseases, adjusted for multiple covariates. We further examined the risk of having multiple endocrine diseases using Linear or Logistic Regression models. Then, sequential mediation analyses were performed to assess the contribution of four possible mechanisms (i.e., suboptimal socioeconomic status (SES), psychological adversities, unfavorable lifestyle, and biological alterations) on the observed associations. RESULTS: During an average follow-up of 30.8 years, 20,885 participants received a diagnosis of endocrine diseases. We observed an association between the cumulative number of experienced childhood maltreatment and increased risk of being diagnosed with any endocrine disease (adjusted hazard ratio (HR) = 1.10, 95% confidence interval 1.09-1.12). The HR was 1.26 (1.22-1.30) when comparing individuals ≥ 2 with those with < 2 experienced childhood maltreatment. We further noted the most pronounced associations for type 2 diabetes (1.40 (1.33-1.48)) and hypothalamic-pituitary-adrenal (HPA)-axis-related endocrine diseases (1.38 (1.17-1.62)), and the association was stronger for having multiple endocrine diseases, compared to having one (odds ratio (95% CI) = 1.24 (1.19-1.30), 1.35 (1.27-1.44), and 1.52 (1.52-1.53) for 1, 2, and ≥ 3, respectively). Sequential mediation analyses showed that the association between childhood maltreatment and endocrine diseases was consistently and most distinctly mediated by psychological adversities (15.38 ~ 44.97%), while unfavorable lifestyle (10.86 ~ 25.32%) was additionally noted for type 2 diabetes whereas suboptimal SES (14.42 ~ 39.33%) for HPA-axis-related endocrine diseases. CONCLUSIONS: Our study demonstrates that adverse psychological sequel of childhood maltreatment constitutes the main pathway to multiple endocrine diseases, particularly type 2 diabetes and HPA-axis-related endocrine diseases. Therefore, increased access to evidence-based mental health services may also be pivotal in reducing the risk of endocrine diseases among childhood maltreatment-exposed individuals.

Cohort profile: the China surgery and anesthesia cohort (CSAC).

The China Surgery and Anaesthesia Cohort (CSAC) study was launched in July 2020 and is an ongoing prospective cohort study recruiting patients aged 40-65 years who underwent elective surgeries with general anaesthesia across four medical centres in China. The general objective of the CSAC study is to improve our understanding of the complex interaction between environmental and genetic components as well as to determine their effects on a wide range of interested surgery/anaesthesia-related outcomes. To achieve this goal, we collected enriched phenotypic data, e.g., sociodemographic characteristics, lifestyle factors, perioperative neuropsychological changes, anaesthesia- and surgery-related complications, and medical conditions, at recruitment, as well as through both active (at 1, 3, 7 days and 1, 3, 6, 12 months after surgery) and passive (for more than 1 year after surgery) follow-up assessments. We also obtained omics data from blood samples. In addition, COVID-19-related information was collected from all participants since January 2023, immediately after COVID-19 restrictions were eased in China. As of July 18, 2023, 12,766 participants (mean age = 52.40 years, 57.93% were female) completed baseline data collection (response rate = 94.68%), among which approximately 70% donated blood and hair samples. The follow-up rates within 12 months after surgery were > 92%. Our initial analyses have demonstrated the incidence of and risk factors for chronic postsurgical pain (CPSP) and postoperative cognitive dysfunction (POCD) among middle-aged Chinese individuals, which may prompt further mechanistic exploration and facilitate the development of effective interventions for preventing those conditions. Additional studies, such as genome-wide association analyses for identifying the genetic determinants of CPSP and POCD, are ongoing, and their findings will be released in the future.

Tuning the Inter-Nanoplatelet Distance and Coupling Strength by Thermally Induced Ligand Decomposition.

CdSe nanoplatelets (NPLs) are promising 2D semiconductors for optoelectronic applications, in which efficient charge transport properties are desirable. It is reported that thermal annealing constitutes an effective strategy to control the optical absorption and electrical properties of CdSe NPLs by tuning the inter-NPL distance. Combining optical absorption, transmission electron microscopy, and thermogravimetric analysis, it is revealed that the thermal decomposition of ligands (e.g., cadmium myristate) governs the inter-NPL distance and thus the inter-NPL electronic coupling strength. Employing ultrafast terahertz spectroscopy, it is shown that this enhanced electronic coupling increases both the free carrier generation efficiency and the short-range mobility in NPL solids. The results show a straightforward method of controlling the interfacial electronic coupling strength for developing functional optoelectronic devices through thermal treatments.

Advances in the Application of Microneedles in the Treatment of Local Organ Diseases.

In recent years, microneedles (MNs) have attracted a lot of attention due to their microscale sizes and high surface area (500-1000 µm in length), allowing pain-free and efficient drug delivery through the skin. In addition to the great success of MNs based transdermal drug delivery, especially for skin diseases, increasing studies have indicated the expansion of MNs to diverse nontransdermal applications, including the delivery of therapeutics for hair loss, ocular diseases, and oral mucosal. Here, the current treatment of hair loss, eye diseases, and oral disease is discussed and an overview of recent advances in the application of MNs is provided for these three noncutaneous localized organ diseases. Particular emphasis is laid on the future trend of MNs technology development and future challenges of expanding the generalizability of MNs.

Comparison of quinoa and highland barley derived dietary fibers influence on the physicochemical properties and digestion of rice starch.

This study investigated the potential of highland barley and quinoa dietary fibers, rich in ß-glucan and pectin respectively, as cost-effective and nutritionally valuable physical modifiers for rice starch (RS). HPAEC revealed differences between the monosaccharide composition of soluble and insoluble dietary fibers sourced from highland barley and quinoa (HSDF, HIDF, QSDF and QIDF). Results from both RVA and DSC analysis revealed that the addition of low amounts of dietary fiber significantly modified the pasting properties of RS. Notably, the addition of quinoa soluble dietary fiber (QSDF) significantly inhibits the formation of a stable gel network in rice starch, even at low concentrations (0.1 %), as confirmed by rheological measurements. Furthermore, the incorporation of QSDF effectively reduces the content of rapidly digestible starch in rice starch by 15.6 % and increases the content of slowly digestible starch, from 23.36 % ± 3.02 % to 31.07 % ± 3.98 %. By leveraging the compositional richness of these fibers, this research opens up novel opportunities for developing functional food products with improved nutritional profiles, as well as for improving texture and reducing glycemic index (GI) in starch-based foods.

An artificial intelligence based news feature mining system based on the Internet of Things and multi-sensor fusion.

The application of Internet of Things (IoT) technology in news media communication has significantly enhanced the effectiveness and coverage of news data releases. However, as the scale of news data continues to grow, traditional IoT approaches face challenges such as slow data processing speed and low mining efficiency. To address these issues, a novel news feature mining system combining IoT and Artificial Intelligence (AI) has been developed. The hardware components of the system include a data collector, a data analyzer, a central controller, and sensors. The GJ-HD data collector is utilized to gather news data. Multiple network interfaces are designed at the device terminal to ensure data extraction from the internal disk in case of device failure. The central controller integrates the MP/MC and DCNF interfaces for seamless information interconnection. In the software aspect of the system, the network transmission protocol of the AI algorithm is embedded, and a communication feature model is constructed. This enables fast and accurate mining of news data communication features. Experimental results demonstrate that the system achieves a mining accuracy of over 98%, enabling efficient processing of news data. Overall, the proposed IoT and AI-based news feature mining system overcomes the limitations of traditional approaches, allowing for efficient and accurate processing of news data in a rapidly expanding digital landscape.

Clinical Characteristics and Economic Burden of Asthma in China: A Multicenter Retrospective Study.

Asthma is a common chronic airway inflammation that produces a healthcare burden on the economy. We aim to obtain a better understanding of the clinical status and disease burden of patients with asthma in China. A retrospective study was carried out based on the computerized medical records in the Jinan Health Medical Big Data Platform between 2011 and 2019 (available data from 38 hospitals). The asthma severity of each patient was assessed retrospectively and categorized as mild, moderate, or severe according to Global Initiative for Asthma 2020 (GINA 2020). The results revealed that the majority (75.0%) of patients suffered from mild asthma. Patients treated with inhaled corticosteroids (ICS)/long-acting beta-agonists (LABA) at emergency department visits had lower frequencies of exacerbations compared with non-ICS/LABA-treated patients. The incidence rates for 1, 2, 3, and 4 exacerbation of the patients treated with ICS/LABA are lower than those treated without ICS/LABA (14.49 vs. 15.01%, 11.94% vs. 19.12%, 6.51% vs.12.92% and 4.10% vs. 9.35%). The difference got a statistical significance Chronic obstructive pulmonary disease (COPD) and gastroesophageal reflux disease (GERD), two comorbidities related to asthma, were risk factors for asthma exacerbation. Finally, patients who suffered from exacerbations produced a heavier economic burden compared to the patients who never suffered exacerbations (mean costs are ï¿¥3,339.67 vs. ï¿¥968.45 separately).  These results provide a reference for clinicians and patients to obtain a better treatment and therapy strategy management for people living with asthma.

Explainable machine-learning algorithms to differentiate bipolar disorder from major depressive disorder using self-reported symptoms, vital signs, and blood-based markers.

BACKGROUND AND OBJECTIVE: Caused by shared genetic risk factors and similar neuropsychological symptoms, bipolar disorder (BD) and major depressive disorder (MDD) are at high risk of misdiagnosis, which is associated with ineffective treatment and worsening of outcomes. We aimed to develop a machine learning (ML)-based diagnostic system, based on electronic medical records (EMR) data, to mimic the clinical reasoning of human physicians to differentiate MDD and BD (especially BD depressive episodes) patients about to be admitted to a hospital and, hence, reduce the misdiagnosis of BD as MDD on admission. In addition, we examined to what extent our ML model could be made interpretable by quantifying and visualizing the features that drive the predictions. METHODS: By identifying 16,311 patients admitted to a hospital located in western China between 2009 and 2018 with a recorded main diagnosis of MDD or BD, we established three sub-cohorts with different combinations of features for both the MDD-BD cohort and the MDD-BD depressive episodes cohort, respectively. Four different ML algorithms (logistic regression, extreme gradient boosting (XGBoost), random forest, and support vector machine) and four train-test splits were used to train and validate diagnostic models, and explainable methods (SHAP and Break Down) were utilized to analyze the contribution of each of the features at both population-level and individual-level, including feature importance, feature interaction, and feature effect on prediction decision for a specific subject. RESULTS: The XGBoost algorithm provided the best test performance (AUC: 0.838 (0.810-0.867), PPV: 0.810 and NPV: 0.834) for separating patients with BD from those with MDD. Core predictors included symptoms (mood-up, exciting, bad sleep, loss of interest, talking, mood-down, provoke), along with age, job, myocardial enzyme markers (creatine kinase, hydroxybutyrate dehydrogenase), diabetes-associated marker (glucose), bone function marker (alkaline phosphatase), non-enzymatic antioxidant (uric acid), markers of immune/inflammation (white blood cell count, lymphocyte count, basophil percentage, monocyte count), cardiovascular function marker (low density lipoprotein), renal marker (total protein), liver biochemistry marker (indirect bilirubin), and vital signs like pulse. For separating patients with BD depressive episodes from those with MDD, the test AUC was 0.777 (0.732-0.822), with PPV 0.576 and NPV 0.899. Additional validation in models built with self-reported symptoms removed from the feature set, showed test AUC of 0.701 (0.666-0.736) for differentiating BD and MDD, and AUC of 0.564 (0.515-0.614) for detecting patients in BD depressive episodes from MDD patients. Validation in the datasets without removing the patients with comorbidity showed an AUC of 0.826 (0.806-0.846). CONCLUSION: The diagnostic system accurately identified patients with BD in various clinical scenarios, and differences in patterns of peripheral markers between BD and MDD could enrich our understanding of potential underlying pathophysiological mechanisms of them.

DNA Assembly of Plasmonic Nanostructures Enables In Vivo SERS-Based MicroRNA Detection and Tumor Photoacoustic Imaging.

Controllable self-assembly of the DNA-linked gold nanoparticle (AuNP) architecture for in vivo biomedical applications remains a key challenge. Here, we describe the use of the programmed DNA tetrahedral structure to control the assembly of three different types of AuNPs (∼20, 10, and 5 nm) by organizing them into defined positioning and arrangement. A DNA-assembled "core-satellite" architecture is built by DNA sequencing where satellite AuNPs (10 and 5 nm) surround a central core AuNP (20 nm). The density and arrangement of the AuNP satellites around the core AuNP were controlled by tuning the size and amount of the DNA tetrahedron functionalized on the core AuNPs, resulting in strong electromagnetic field enhancement derived from hybridized plasmonic coupling effects. By conjugating with the Raman molecule, strong surface-enhanced Raman scattering photoacoustic imaging signals could be generated, which were able to image microRNA-21 and tumor tissues in vivo. These results provided an efficient strategy to build precision plasmonic superstructures in plasmonic-based bioanalysis and imaging.

Near-Unity Energy Transfer from Uranyl to Europium in a Heterobimetallic Organic Framework with Record-Breaking Quantum Yield.

Lanthanide organic frameworks (Ln-MOFs) have attracted increasing research enthusiasm as photoluminescent materials. However, limited luminescence efficiency stemming from restricted energy transfer efficiency from the organic linker to the metal center hinders their applications. Herein, a uranyl sensitization approach was proposed to boost the luminescence efficiency of Ln-MOFs in a distinct heterobimetallic uranyl-europium organic framework. The record-breaking photoluminescence quantum yield (PLQY, 92.68%) among all reported Eu-MOFs was determined to benefit from nearly 100% energy transfer efficiency between UO22+ and Eu3+. Time-dependent density functional theory and ab initio wave-function theory calculations confirmed the overlap of excited state levels between UO22+ and Eu3+, which is responsible for the efficient energy transfer process. Coupled with intrinsically strong stopping power toward X-ray of the uranium center, SCU-UEu-2 features an ultralow detection limit of 1.243 µGyair/s, outperforming the commercial scintillator LYSO (13.257 µGyair/s) and satisfying the requirement of X-ray diagnosis (below 5.5 µGyair/s) in full.

The impact of COVID-19 lockdown on nursing higher education at Chengdu University.

BACKGROUND: To combat/control the COVID-19 pandemic, a complete lockdown was implemented in China for almost 6 months during 2020. PURPOSE: To determine the impact of a long-term lockdown on the academic performance of first-year nursing students via mandatory online learning, and to determine the benefits of online teaching. METHODS: The recruitment and academic performance of 1st-year nursing students were assessed between 2019 [prior to COVID-19, n = 195, (146 women)] and 2020 [during COVID-19, n = 180 (142 women)]. The independent sample t test or Mann-Whitney test was applied for a comparison between these two groups. RESULTS: There was no significant difference in student recruitment between 2019 and 2020. The overall performance of the first-year students improved in the Biochemistry, Immunopathology, Traditional Chinese Medicine Nursing and Combined Nursing courses via mandatory online teaching in 2020 compared with traditional teaching in 2019. CONCLUSION: Suspension of in-class learning but continuing education virtually online has occurred without negatively impacting academic performance, thus academic goals are more than achievable in a complete lockdown situation. This study offers firm evidence to forge a path for developments in teaching methods to better incorporate virtual learning and technology in order to adapt to fast-changing environments. However, the psychological/psychiatric and physical impact of the COVID-19 lockdown and the lack of face-to-face interaction on these students remains to be explored.

Impact and Cost-Effectiveness of Biomedical Interventions on Adult Hepatitis B Elimination in China: A Mathematical Modelling Study.

BACKGROUND: China has one of the highest hepatitis B virus (HBV) disease burdens worldwide and tracking progress toward the 2030 HBV elimination targets is essential. This study aimed to assess the impact of biomedical interventions (i.e., adult vaccination, screening and treatment) on the adult HBV epidemic, estimate the time for HBV elimination, and evaluate the cost-effectiveness of the interventions in China. METHODS: A deterministic compartmental model was developed to project the HBV epidemic from 2022 to 2050 and estimate the time to meet elimination targets under four intervention scenarios. Cost-effectiveness was calculated using incremental cost per quality-adjusted life year (QALY) gained, i.e., average cost-effectiveness ratio (CER). RESULTS: Under the status quo, there will be 42.09-45.42 million adults living with HBV in 2050 and 11.04-14.36 million HBV-related deaths cumulatively from 2022 to 2050. Universal vaccination would cumulatively avert 3.44-3.95 million new cases at a cost of US$1027-1261/QALY gained. The comprehensive strategy would cumulatively avert 4.67-5.24 million new chronic cases and 1.39-1.85 million deaths, expediting the realization of the elimination targets forward to 2049. This strategy was also cost-effective with an average CER of US$20,796-26,685/QALY and a saved healthcare cost of US$16.10-26.84 per person. CONCLUSION: China is not on track to meet the elimination targets but comprehensive biomedical interventions can accelerate the realization of the targets. A comprehensive strategy is cost-effective and cost-saving, which should be promoted in primary care infrastructures. Universal adult vaccination may be appropriate in the near future considering practical feasibility.

Automated detection of hippocampal sclerosis using real-world clinical MRI images.

Background: Hippocampal sclerosis (HS) is the most common pathological type of temporal lobe epilepsy (TLE) and one of the important surgical markers. Currently, HS is mainly diagnosed manually by radiologists based on visual inspection of MRI, which greatly relies on MRI quality and physician experience. In clinical practice, non-thin MRI scans are often used due to the time and efficiency needed for the acquisition. However, these scans can be difficult for junior physicians to interpret accurately. Thus, the rapid and accurate diagnosis of HS using real-world MRI images in clinical settings is a challenging task. Objective: Our aim was to explore the feasibility of using computer vision methods to diagnose HS on real-world clinical MRI images and to provide a reference for future clinical applications of artificial intelligence methods to aid in detecting HS. Methods: We proposed a deep learning algorithm called "HS-Net" to discriminate HS using real-world clinical MRI images. First, we delineated and segmented a region of interest (ROI) around the hippocampus. Then, we utilized the fractional differential (FD) method to enhance the textures of the ROIs. Finally, we used a small-sample image classification method based on transfer learning to fine-tune the feature extraction part of a pretrained model and added two fully connected layers and an output layer. In the study, 96 TLE patients with HS confirmed by postoperative pathology and 89 healthy controls were retrospectively enrolled. All subjects were cross-validated, and models were evaluated for performance, robustness, and clinical utility. Results: The HS-Net model achieved an area under the curve (AUC) of 0.894, an accuracy of 82.88%, an F1-score of 84.08% in the test cohort based on real, routine, clinical T2-weighted fluid attenuated inversion recovery (FLAIR) sequence MRI images. Additionally, the AUC, accuracy and F1 scores of our model all increased by around 3 percentage points when the inputs were augmented with the ROIs of the textures enhanced using the FD method. Conclusions: Our computational model has the potential to be used for the diagnosis of HS in real clinical MRI images, which could assist physicians, particularly junior physicians, in improving the accuracy of discrimination.

Emergence of a Lanthanide Chalcogenide as an Ideal Scintillator for a Flexible X-ray Detector.

The development of high-performance X-ray detectors requires scintillators with fast decay time, high light yield, stability, and X-ray absorption capacity, which are difficult to achieve in a single material. Here, we present the first example of a lanthanide chalcogenide of LaCsSiS4 : 1 % Ce3+ that simultaneously integrates multiple desirable properties for an ideal scintillator. LaCsSiS4 : 1 % Ce3+ demonstrates a remarkably low detection limit of 43.13 nGyair s-1 and a high photoluminescence quantum yield of 98.24 %, resulting in a high light yield of 50480±1441 photons/MeV. Notably, LaCsSiS4 : 1 % Ce3+ exhibits a fast decay time of only 29.35±0.16 ns, making it one of the fastest scintillators among all lanthanide-based inorganic scintillators. Furthermore, this material shows robust radiation and moisture resistance, endowing it with suitability for chemical processing under solution conditions. To demonstrate the X-ray imaging capacity of LaCsSiS4 : 1 % Ce3+ , we fabricated a flexible X-ray detector that achieved a high spatial resolution of 8.2 lp mm-1 . This work highlights the potential of lanthanide chalcogenide as a promising candidate for high-performance scintillators.

Immune checkpoint inhibitor-associated toxicity in advanced non-small cell lung cancer: An updated understanding of risk factors.

Immune checkpoint inhibitors (ICIs), such as programmed death-1 (PD-1), programmed death-ligand 1 (PD-L1), cytotoxic T lymphocyte antigen 4 (CTLA-4) antibodies, etc, have revolutionized cancer treatment strategies, including non-small cell lung cancer (NSCLC). While these immunotherapy agents have achieved durable clinical benefits in a subset of NSCLC patients, they bring in a variety of immune-related adverse events (irAEs), which involve cardiac, pulmonary, gastrointestinal, endocrine and dermatologic system damage, ranging from mild to life-threatening. Thus, there is an urgent need to better understand the occurrence of irAEs and predict patients who are susceptible to those toxicities. Herein, we provide a comprehensive review of what is updated about the clinical manifestations, mechanisms, predictive biomarkers and management of ICI-associated toxicity in NSCLC. In addition, this review also provides perspective directions for future research of NSCLC-related irAEs.

Prediction of Suicidal Behaviors in the Middle-aged Population: Machine Learning Analyses of UK Biobank.

BACKGROUND: Suicidal behaviors, including suicide deaths and attempts, are major public health concerns. However, previous suicide models required a huge amount of input features, resulting in limited applicability in clinical practice. OBJECTIVE: We aimed to construct applicable models (ie, with limited features) for short- and long-term suicidal behavior prediction. We further validated these models among individuals with different genetic risks of suicide. METHODS: Based on the prospective cohort of UK Biobank, we included 223 (0.06%) eligible cases of suicide attempts or deaths, according to hospital inpatient or death register data within 1 year from baseline and randomly selected 4460 (1.18%) controls (1:20) without such records. We similarly identified 833 (0.22%) cases of suicidal behaviors 1 to 6 years from baseline and 16,660 (4.42%) corresponding controls. Based on 143 input features, mainly including sociodemographic, environmental, and psychosocial factors; medical history; and polygenic risk scores (PRS) for suicidality, we applied a bagged balanced light gradient-boosting machine (LightGBM) with stratified 10-fold cross-validation and grid-search to construct the full prediction models for suicide attempts or deaths within 1 year or between 1 and 6 years. The Shapley Additive Explanations (SHAP) approach was used to quantify the importance of input features, and the top 20 features with the highest SHAP values were selected to train the applicable models. The external validity of the established models was assessed among 50,310 individuals who participated in UK Biobank repeated assessments both overall and by the level of PRS for suicidality. RESULTS: Individuals with suicidal behaviors were on average 56 years old, with equal sex distribution. The application of these full models in the external validation data set demonstrated good model performance, with the area under the receiver operating characteristic (AUROC) curves of 0.919 and 0.892 within 1 year and between 1 and 6 years, respectively. Importantly, the applicable models with the top 20 most important features showed comparable external-validated performance (AUROC curves of 0.901 and 0.885) as the full models, based on which we found that individuals in the top quintile of predicted risk accounted for 91.7% (n=11) and 80.7% (n=25) of all suicidality cases within 1 year and during 1 to 6 years, respectively. We further obtained comparable prediction accuracy when applying these models to subpopulations with different genetic susceptibilities to suicidality. For example, for the 1-year risk prediction, the AUROC curves were 0.907 and 0.885 for the high (>2nd tertile of PRS) and low (<1st) genetic susceptibilities groups, respectively. CONCLUSIONS: We established applicable machine learning-based models for predicting both the short- and long-term risk of suicidality with high accuracy across populations of varying genetic risk for suicide, highlighting a cost-effective method of identifying individuals with a high risk of suicidality.

Trends in incident diagnoses and drug prescriptions for anxiety and depression during the COVID-19 pandemic: an 18-month follow-up study based on the UK Biobank.

Serious concerns have been raised about the negative effects of the COVID-19 pandemic on population psychological well-being. However, limited data exist on the long-term effects of the pandemic on incident psychiatric morbidities among individuals with varying exposure to the pandemic. Leveraging prospective data from the community-based UK Biobank cohort, we included 308,400 participants free of diagnosis of anxiety or depression, as well as 213,757 participants free of anxiolytics or antidepressants prescriptions, to explore the trends in incident diagnoses and drug prescriptions for anxiety and depression from 16 March 2020 to 31 August 2021, compared to the pre-pandemic period (i.e., 1 January 2017 to 31 December 2019) and across populations with different exposure statuses (i.e., not tested for COVID-19, tested negative and tested positive). The age- and sex-standardized incidence ratios (SIRs) were calculated by month which indicated an increase in incident diagnoses of anxiety or depression among individuals who were tested for COVID-19 (tested negative: SIR 3.05 [95% confidence interval 2.88-3.22]; tested positive: 2.03 [1.76-2.34]), especially during the first six months of the pandemic (i.e., March-September 2020). Similar increases were also observed for incident prescriptions of anxiolytics or antidepressants (tested negative: 1.56 [1.47-1.67]; tested positive: 1.41 [1.22-1.62]). In contrast, individuals not tested for COVID-19 had consistently lower incidence rates of both diagnoses of anxiety or depression (0.70 [0.67-0.72]) and prescriptions of respective psychotropic medications (0.70 [0.68-0.72]) during the pandemic period. These data suggest a distinct rise in health care needs for anxiety and depression among individuals tested for COVID-19, regardless of the test result, in contrast to a reduction in health care consumption for these disorders among individuals not tested for and, presumably, not directly exposed to the disease.