Obstructive sleep apnea and mental disorders: a bidirectional mendelian randomization study.

BACKGROUND: Previous studies have reported associations between obstructive sleep apnea (OSA) and several mental disorders. However, further research is required to determine whether these associations are causal. Therefore, we evaluated the bidirectional causality between the genetic liability for OSA and nine mental disorders by using Mendelian randomization (MR). METHOD: We performed two-sample bidirectional MR of genetic variants for OSA and nine mental disorders. Summary statistics on OSA and the nine mental disorders were extracted from the FinnGen study and the Psychiatric Genomics Consortium. The primary analytical approach for estimating causal effects was the inverse-variance weighted (IVW), with the weighted median and MR Egger as complementary methods. The MR Egger intercept test, Cochran's Q test, Rucker's Q test, and the MR pleiotropy residual sum and outlier (MR-PRESSO) test were used for sensitivity analyses. RESULT: MR analyses showed that genetic liability for major depressive disorder (MDD) was associated with an increased risk of OSA (odds ratio [OR] per unit increase in the risk of MDD, 1.29; 95% CI, 1.11-1.49; P < 0.001). In addition, genetic liability for OSA may be associated with an increased risk of attention-deficit/hyperactivity disorder (ADHD) (OR = 1.26; 95% CI, 1.02-1.56; p = 0.032). There was no evidence that OSA is associated with other mental disorders. CONCLUSION: Our study indicated that genetic liability for MDD is associated with an increased risk of OSA without a bidirectional relationship. Additionally, there was suggestive evidence that genetic liability for OSA may have a causal effect on ADHD. These findings have implications for prevention and intervention strategies targeting OSA and ADHD. Further research is needed to investigate the biological mechanisms underlying our findings and the relationship between OSA and other mental disorders.

Trends in antidiabetic drug use and expenditure in public hospitals in Northwest China, 2012-21: a case study of Gansu Province.

BACKGROUND: Since the twenty-first century, the prevalence of diabetes has risen globally year by year. In Gansu Province, an economically underdeveloped province in northwest China, the cost of drugs for diabetes patients accounted for one-third of their total drug costs. To fundamentally reduce national drug expenditures and the burden of medication on the population, the relevant departments of government have continued to reform and improve drug policies. This study aimed to analyse long-term trends in antidiabetic drug use and expenditure in Gansu Province from 2012 to 2021 and to explore the role of pharmaceutical policy. METHODS: Data were obtained from the provincial centralised bidding and purchasing (CBP) platform. Drug use was quantified using the anatomical therapeutic chemistry/defined daily dose (ATC/DDD) method and standardised by DDD per 1000 inhabitants per day (DID), and drug expenditure was expressed in terms of the total amount and defined daily cost (DDC). Linear regression was used to analyse the trends and magnitude of drug use and expenditure. RESULTS: The overall trend in the use and expenditure of antidiabetic drugs was on the rise, with the use increasing from 1.04 in 2012 to 16.02 DID in 2021 and the expenditure increasing from 48.36 in 2012 to 496.42 million yuan in 2021 (from 7.66 to 76.95 million USD). Some new and expensive drugs changed in the use pattern, and their use and expenditure shares (as the percentage of all antidiabetic drugs) increased from 0 to 11.17% and 11.37%, but insulins and analogues and biguanides remained the most used drug class. The DDC of oral drugs all showed a decreasing trend, but essential medicines (EMs) and medical insurance drugs DDC gradually decreased with increasing use. The price reduction of the bid-winning drugs was over 40%, and the top three drugs were glimepiride 2mg/30, acarbose 50mg/30 and acarbose 100mg/30. CONCLUSIONS: The implementation of pharmaceutical policies has significantly increased drug use and expenditure while reducing drug prices, and the introduction of novel drugs and updated treatment guidelines has led to changes in use patterns.

MOF-mediated acetylation of UHRF1 enhances UHRF1 E3 ligase activity to facilitate DNA methylation maintenance.

The multi-domain protein UHRF1 (ubiquitin-like, containing PHD and RING finger domains, 1) recruits DNMT1 for DNA methylation maintenance during DNA replication. Here, we show that MOF (males absent on the first) acetylates UHRF1 at K670 in the pre-RING linker region, whereas HDAC1 deacetylates UHRF1 at the same site. We also identify that K667 and K668 can also be acetylated by MOF when K670 is mutated. The MOF/HDAC1-mediated acetylation in UHRF1 is cell-cycle regulated and peaks at G1/S phase, in line with the function of UHRF1 in recruiting DNMT1 to maintain DNA methylation. In addition, UHRF1 acetylation significantly enhances its E3 ligase activity. Abolishing UHRF1 acetylation at these sites attenuates UHRF1-mediated H3 ubiquitination, which in turn impairs DNMT1 recruitment and DNA methylation. Taken together, these findings identify MOF as an acetyltransferase for UHRF1 and define a mechanism underlying the regulation of DNA methylation maintenance through MOF-mediated UHRF1 acetylation.

Non-radical activation of persulfate with Bi2O3/BiO1.3I0.4 for efficient degradation of propranolol under visible light.

Non-radical activation of persulfate (PS) by photocatalysts is an effective approach for removing organic pollutants from aqueous environments. In this study, a novel Bi2O3/BiO1.3I0.4 heterojunction was synthesized using a facile solvothermal approach and used for the first time for non-radical activation of PS to degrade propranolol (PRO) in the presence of visible light. The findings found that the degradation rate of PRO in the Bi2O3/BiO1.3I0.4/PS system was significantly increased from 19% to more than 90% within 90 min compared to the Bi2O3/BiO1.3I0.4 system. This indicated that the composite system exerted an excellent synergistic effect between the photocatalyst and the persulfate-based oxygenation. Quenching tests and electron paramagnetic resonance demonstrated that the non-radical pathway with singlet oxygen as the active species played a major role in the photocatalytic process. The existence of photo-generated holes during the reaction could also be directly involved in the oxidation of pollutants. Meanwhile, a possible PRO degradation pathway was also proposed. Furthermore, the impacts of pH, humic acid and common anions on the PRO degradation by the Bi2O3/BiO1.3I0.4/PS were explored, and the system's stability and reusability were also studied. This study exhibits a highly productive catalyst for PS activation via a non-radical pathway and provides a new idea for the degradation of PRO.

Study on the trend of congenital heart disease inpatient costs and its influencing factors in economically underdeveloped areas of China, 2015-2020: a case study of Gansu Province.

Background: Economic data on congenital heart disease are scarce in economically underdeveloped areas of China. Therefore, this study aimed to shed light on the level and changing trend of congenital heart disease inpatients' economic burden in underdeveloped areas. Method: This study used a multi-stage stratified cluster sampling method to select 11,055 inpatients with congenital heart disease from 197 medical and health institutions in Gansu Province. Their medical records and expenses were obtained from the Hospital Information System. Univariate analysis was conducted using the rank sum test and Spearman rank correlation. Quantile regression and random forest were used to analyze the influencing factors. Results: From 2015 to 2020, the average length of stay for congenital heart disease patients in Gansu Province was 10.09 days, with an average inpatient cost of USD 3,274.57. During this period, the average inpatient costs per time increased from USD 3,214.85 to USD 3,403.41, while the average daily inpatient costs increased from USD 330.05 to USD 376.56. The average out-of-pocket costs per time decreased from USD 2,305.96 to USD 754.77. The main factors that affected the inpatient costs included length of stay, cardiac procedure, proportion of medications, age, and hospital level. Conclusion: Congenital heart disease causes a significant economic burden on both families and society. Therefore, to further reduce the patient's financial burden, the length of stay should be reasonably reduced, and the rational distribution of medical resources should be continuously promoted to ensure equitable access to healthcare services.

Introduced Hierarchically Ordered Porous Architecture on a Separator as an Efficient Polysulfide Trap toward High-Mass-Loading Li-S Batteries.

The severe shuttle effect and the depletion of active sulfur result in performance deterioration, presenting two formidable issues that must be overcome to achieve high-mass-loading lithium-sulfur batteries. Herein, we reported a composite separator by introducing carbon photonic crystals with a hierarchically ordered porous structure on a commercial separator. The ordered structure and interconnected hierarchical macro-meso-micropore network of the composite separator facilitate efficient trapping of polysulfides and rapid transport of lithium ions. The high ion diffusivity promotes the conversion of polysulfides enhancing sulfur utilization and mitigating the occurrence of "dead sulfur" on the surface of the separator. Impressively, under a high sulfur loading of 3 mg cm-2, the lithium-sulfur battery with the composite separator displayed a high reversible capacity of 1582 mA h g-1 at 0.1 C and an excellent long-term cycling performance with a decay rate of as low as 0.033% per cycle over 1500 cycles at 1 C. Surprisingly, the battery represented a high reversible capacity of 935 mA h g-1 at 0.2 C even at a sulfur loading of 6.71 mg cm-2. The design of the composite separator underscores the pivotal role of carbon architecture in improving battery performance and brings a bright prospect to enable the commercialization of high-mass-loading lithium-sulfur batteries.

A precisely Assembled Wall-Like Architecture for High Lithium/Sodium Storage.

MXene nanosheets and ordered porous carbons both have their own advantages and disadvantages. Assembling and combining the advantages of the two will be a good choice for battery electrode hosts of active materials. In this work, an electrostatic separation-adsorption strategy is proposed to realize the ordered alternating self-assembly of MXene nanosheets and ordered porous carbon (MPOC), obtaining a unique wall-like porous material with a high conductivity and interconnected porous nanostructure, which strengthens the transfer rate of electrons and ions simultaneously. Meanwhile, the introduction of N-doping from porous carbon into MPOC prolongs the cycle life. When use red phosphorus (RP) as active materials, the MPOC@RP anode exhibited high-capacity output (2454.3 and 2408.1 mAh g-1 in lithium-ion batteries (LIBs) and sodium-ion batteries (SIBs) at 0.1 C) and long cycle life (the decay rates per cycle of 0.028% and 0.036% after 1500 and 1200 cycles at 2 C in LIBs and SIBs respectively). The successful application in RP anodes displays great potential in other electrode materials such as silicon, sulfur, selenium, and so on. Meanwhile, this strategy is also effective to design other composites materials like MXene and carbon nanotubes, MXene and Graphene, and so on.

Association and interactions between mixed exposure to trace elements and the prevalence of kidney stones: a study of NHANES 2017-2018.

Background: The association between exposure to trace elements mixture and the prevalence of kidney stones and the interactions between elements are unclear. The aim of this study was to explore the association between exposure to trace elements mixture and the prevalence of kidney stones and the interactions between the elements. Methods: A total of 1,244 participants (139 kidney stone formers and 1,105 non-stone former participants) in NHANES 2017-2018 were included. The exposure to trace elements was evaluated by measuring their concentration in urine samples. Three methods, Logistic regression, quantile-based g computation (qgcomp), and Bayesian kernel machine regression (BKMR), were used for analysis. Results: According to the results from qgcomp and BKMR, a negative association was found between exposure to the 13 trace elements and the prevalence of kidney stones [OR = 0.50 (0.32, 0.78)]. Subgroup analysis revealed that Co, As, and iodine in the whole population, Co, As, and Ni in males, and Cs, iodine, and Sb in females, were most strongly associated with kidney stones. Kidney stone was found to be positively correlated with Co and negatively correlated with the other elements. Besides, there were significant interactions between Ni and Pb in the whole population, Co and iodine in males, and Pb and iodine in females. Conclusion: There was a negative association between exposure to the mixture of 13 trace elements and the prevalence of kidney stones.

Exploring the factors influencing the use of health services by people with diabetes in Northwest China: an example from Gansu Province.

BACKGROUND: Diabetes is associated with high morbidity, mortality and quality-of-life impairment in patients. In China, the number of people suffering from diabetes ranks first in the world. Gansu Province is located in northwest China and is an economically underdeveloped region of China. By analyzing the level of health service utilization of people with diabetes in Gansu Province, the degree of equity in health service utilization and its influencing factors were studied to provide scientific data to support the promotion of health equity for people with diabetes and the introduction of relevant policies by relevant authorities. METHODS: A sample of 282 people with diabetes who were 15 years old and above was chosen by multi-stage stratified sampling method. A structured questionnaire survey was conducted via face-to-face interviews. Random forest and logistic regression analysis were used to demonstrate the effects of the explanatory variables on health seeking behaviors from predisposing, enabling and need variables. The concentration index was used to indicate the equity of health service utilization across households of different economic levels. RESULTS: The outpatient rate for the diabetic population surveyed was 92.91%, with 99.87% of urban patients, higher than the 90.39% of rural patients. The average number of hospital days per person was 3.18 days, with 5.03 days per person in urban areas, which was higher than the 2.51 days per person in rural areas. The study showed that the factors most likely to influence patients to seek outpatient services were frequency of taking diabetic medication, whether or not they were contracted to a household doctor, and living environment; the top three factors most likely to influence patients with diabetes to seek inpatient services were number of non-communicable chronic disease, self-assessment of health status, medical insurance. The concentration index for outpatient service utilization and inpatient service utilization were - 0.241 and 0.107, respectively, indicating that outpatient services were concentrated on patients at lower income levels and patients at higher income levels tended to favor inpatient services. CONCLUSION: This study found that the low level of health care resources available to people with diabetes, whose health status is suboptimal, makes it difficult to meet their health needs. Patients' health conditions, comorbidities of people with diabetes, and the level of protection were still important factors that hindered the use of health services. It is necessary to promote the rational use of health services by diabetic patients and further improve the corresponding policies to achieve the goal of chronic disease prevention and control in "Health China 2030".

Automated segmentation of the human supraclavicular fat depot via deep neural network in water-fat separated magnetic resonance images.

Background: Human brown adipose tissue (BAT), mostly located in the cervical/supraclavicular region, is a promising target in obesity treatment. Magnetic resonance imaging (MRI) allows for mapping the fat content quantitatively. However, due to the complex heterogeneous distribution of BAT, it has been difficult to establish a standardized segmentation routine based on magnetic resonance (MR) images. Here, we suggest using a multi-modal deep neural network to detect the supraclavicular fat pocket. Methods: A total of 50 healthy subjects [median age/body mass index (BMI) =36 years/24.3 kg/m2] underwent MRI scans of the neck region on a 3 T Ingenia scanner (Philips Healthcare, Best, Netherlands). Manual segmentations following fixed rules for anatomical borders were used as ground truth labels. A deep learning-based method (termed as BAT-Net) was proposed for the segmentation of BAT on MRI scans. It jointly leveraged two-dimensional (2D) and three-dimensional (3D) convolutional neural network (CNN) architectures to efficiently encode the multi-modal and 3D context information from multi-modal MRI scans of the supraclavicular region. We compared the performance of BAT-Net to that of 2D U-Net and 3D U-Net. For 2D U-Net, we analyzed the performance difference of implementing 2D U-Net in three different planes, denoted as 2D U-Net (axial), 2D U-Net (coronal), and 2D U-Net (sagittal). Results: The proposed model achieved an average dice similarity coefficient (DSC) of 0.878 with a standard deviation of 0.020. The volume segmented by the network was smaller compared to the ground truth labels by 9.20 mL on average with a mean absolute increase in proton density fat fraction (PDFF) inside the segmented regions of 1.19 percentage points. The BAT-Net outperformed all implemented 2D U-Nets and the 3D U-Nets with average DSC enhancement ranging from 0.016 to 0.023. Conclusions: The current work integrates a deep neural network-based segmentation into the automated segmentation of supraclavicular fat depot for quantitative evaluation of BAT. Experiments show that the presented multi-modal method benefits from leveraging both 2D and 3D CNN architecture and outperforms the independent use of 2D or 3D networks. Deep learning-based segmentation methods show potential towards a fully automated segmentation of the supraclavicular fat depot.

Factors influencing catastrophic health expenditure of households with people with diabetes in Northwest China-an example from Gansu Province.

BACKGROUND: Diabetes is a chronic non-communicable disease that causes a substantial economic burden on diabetic suffers and their households. The aim of this study was to explore the prevalence, equity, and determinants of catastrophic health expenditure (CHE) among households with people with diabetes in Northwest China. METHODS: A total of 3,000 households were included in the 6th Health services survey in Gansu Province, China of which 270 households with people with diabetes. The equity of CHE was evaluated by concentration curve and concentration index (CI). We adopted the Pareto chart to analyze the main economic intervals of the occurrence of CHE. Finally, we combined the decision tree and logistic model and analyzed the determinants of the occurrence of CHE. RESULTS: The incidence of CHE at 15%, 25% and 40% were 75.19%, 58.89% and 35.19%, respectively. CHE tended to occur in households with a lower economic level, with the phenomenon being more pronounced at Z = 40%. The Pareto chart showed that households in the group with an annual per capita income of 0-740 USD (0-5,000 Chinese Yuan) were most likely to experience CHE. Both decision tree and logistic models suggested that economic level, comorbidities, and small household size were potential risk factors. In addition, the decision tree model also suggested the interaction between the influencing factor of health checks in the past 12 months and the number of chronic diseases. CONCLUSIONS: In summary, Households with people with diabetes were more likely to incur CHE. It is essential to focus on low- and middle-income households with people with diabetes, strengthen the management of patients with diabetes, and provide timely health interventions to reduce the occurrence of chronic comorbidity and the risk of CHE in households.

Research on Early Identification Model of Intravenous Immunoglobulin Resistant Kawasaki Disease Based on Gradient Boosting Decision Tree.

BACKGROUND: To construct a predictive model for intravenous immunoglobulin (IVIG) resistant Kawasaki disease (KD) based on the gradient boosting decision tree (GBDT), so as to early identify children with IVIG resistance and actively take additional treatment to prevent adverse events. METHODS: The case data of KD children hospitalized in the Pediatric Department of Lanzhou University Second Hospital from October 2015 to July 2020 were collected. All KD patients were divided into IVIG responsive group and IVIG resistant group. GBDT was used to explore the influencing factors of IVIG-resistant KD and to construct a prediction model. Then compared with previous models, the optimal model was selected. RESULTS: In the process of GBDT model construction, 80% of the data were used as the test set, and 20% of the data were used as the validation set. Among them, the verification set was used to adjust the hyperparameters in GDBT learning. The model performed best with a hyperparameter tree depth of 5. The area under the curve of the GBDT model constructed based on the best parameters was 0.87 (95% CI: 0.85-0.90), the sensitivity was 72.62%, the specificity was 89.04%, and the accuracy was 61.65%. The contribution degree of each feature value to the model was total bilirubin, albumin, C-reactive protein, fever time, and Na in order. CONCLUSION: The GBDT model is more suitable for the prediction of IVIG-resistant KD in this study area.

Associations of urinary and blood cadmium concentrations with all-cause mortality in US adults with chronic kidney disease: a prospective cohort study.

Epidemiological evidence for the relationship between cadmium exposure and mortality in specific chronic kidney disease (CKD) populations remains scarce. We aimed to explore the relationships between cadmium concentrations in urine and blood and all-cause mortality among CKD patients in the USA. This cohort study was composed of 1825 CKD participants from the National Health and Nutrition Examination Survey (NHANES) (1999-2014) who were followed up to December 31, 2015. All-cause mortality was ascertained by matching the National Death Index (NDI) records. We estimated hazard ratios (HRs) and 95% confidence intervals (CIs) for all-cause mortality in relation to urinary and blood cadmium concentrations by Cox regression models. During an average follow-up period of 82 months, 576 CKD participants died. Compared with the lowest quartiles, HRs (95% CIs) for all-cause mortality associated with the fourth weighted quartiles of urinary and blood cadmium concentrations were 1.75 (1.28 to 2.39) and 1.59 (1.17 to 2.15), respectively. Furthermore, the HRs (95% CIs) for all-cause mortality per ln-transformed IQR increment in cadmium concentrations in urine (1.15 µg/g UCr) and blood (0.95 µg/L) were 1.40 (1.21 to 1.63) and 1.22 (1.07 to 1.40), respectively. Linear concentration-response relationships between urinary and blood cadmium concentrations and all-cause mortality were also found. Our findings suggested that increased cadmium concentrations in both urine and blood significantly contributed to enhanced mortality risk in CKD patients, thus highlighting that efforts to reduce cadmium exposure may reduce mortality risk in high-risk populations with CKD.

The Bidirectional Relationship between Chronic Kidney Disease and Hyperuricemia: Evidence from a Population-Based Prospective Cohort Study.

BACKGROUND: Although several studies have examined the association between chronic kidney disease (CKD) and hyperuricemia (HUA), the direction of the association remains unclear. We aimed to investigate whether there was a bidirectional association between them. METHODS: The present study was conducted in three analyses. Analysis I included 25,433 participants free of HUA at baseline to evaluate the associations between CKD and estimated glomerular filtration rate (eGFR) with incident HUA. Analysis II had 28,422 participants free of CKD at baseline to analyze the relationships between HUA and serum uric acid (sUA) with new-onset CKD. Cox proportional hazards regression models were applied to evaluate the association involved in Analysis I and II. Analysis III included 31,028 participants with complete data and further dissected the bidirectional association between sUA and eGFR using cross-lag models. RESULTS: New-onset HUA and CKD were observed in the first round of the follow-up study among 1597 and 1212 participants, respectively. A significantly higher risk of HUA was observed in individuals with CKD compared to individuals without CKD (HR = 1.58, 95% CI: 1.28-1.95). The adjusted HRs (95% CIs) of HUA were 3.56 (2.50-5.05) for the participants in the group of eGFR less than 60 mL·min-1·1.73 m-2, 1.61 (1.42-1.83) for those in the group of eGFR between 60 and 90 mL·min-1·1.73 m-2, and 1.74 (1.42-2.14) for those in the group of eGFR more than 120 mL·min-1·1.73 m-2, compared with the group of eGFR between 90 and 120 mL·min-1·1.73 m-2. A higher risk of CKD was also observed in individuals with HUA compared to individuals without HUA (HR = 1.28, 95% CI: 1.12-1.47). Compared with the first quintile of sUA, the adjusted HR (95% CI) of CKD was 1.24 (1.01-1.51) for the participants in the fourth quantile. There was a bidirectional relationship between sUA and eGFR, with the path coefficients (ρ1 = -0.024, p < 0.001) from baseline eGFR to follow-up sUA and the path coefficients (ρ2 = -0.015, p = 0.002) from baseline sUA to follow-up eGFR. CONCLUSIONS: The present study indicated that CKD and HUA were closely associated, and there was a bidirectional relationship between sUA and eGFR.

Long-term trend of antibiotic use at public health care institutions in northwest China, 2012-20 -- a case study of Gansu Province.

BACKGROUND: Over the past 20 years, excessive antibiotic use has led to serious antimicrobial resistance (AMR) worldwide, and the phenomenon is particularly serious in China. To this end, the Chinese health sector took a series of measures to promote rational antibiotic use. In this study, to reveal the impact of policies on antibiotic use, we explored the long-term trend and patterns of antibiotic use at public health care institutions from 2012 to 2020 in northwest China, taking Gansu Province as an example. METHODS: Antibiotic procurement data were obtained from the provincial centralized bidding procurement (CBP) platform between 2012 and 2020. Antibiotic use was quantified using the Anatomical Therapeutic Chemical (ATC)/defined daily doses (DDD) methodology and standardized using the DDD per 1000 inhabitants per day (DID). Twelve relevant quality indicators were calculated for comparison with the European Surveillance of Antimicrobial Consumption (ESAC) project monitoring results. RESULTS: Total antibiotic use increased from 18.75 DID to 57.07 DID and then decreased to 19.11 DID, a turning point in 2014. The top three antibiotics used were J01C (beta-lactam antibacterials, penicillins), J01F (macrolides, lincosamides and streptogramins), and J01D (other beta-lactam antibacterials, cephalosporins), accounting for 45.15%, 31.40%, and 11.99% respectively. The oral antibiotics used were approximately 2.5 times the parenteral antibiotics, accounting for 71.81% and 28.19%, respectively. Different use preferences were shown in public hospitals and primary health care centres (PHCs), and the latter accounted for more than half of total use. The absolute use of all classes of antibiotics in Gansu is almost higher than any of the 31 European countries included in the ESAC, but the relative use of some focused antibiotics is lower than theirs. CONCLUSIONS: The intervention policies of the health department reduced antibiotic use in Gansu Province, but the proportion of broad-spectrum and parenteral antibiotics was still high. It is necessary to further improve the quality of antibiotic prescriptions and pay more attention to the rationality of antibiotic use in PHCs.

Hoya-like Hierarchical Porous Architecture as Multifunctional Phosphorus Anode for Superior Lithium-Sodium Storage.

Designing nanostructured hosts with the merits of high conductivity, strong trapping ability, and long-term durability to improve the insulating nature and extreme volume change of red phosphorus (RP) is a promising option for the development of high-performance lithium/sodium-ion batteries (LIBs/SIBs). Here, a multifunctional RP immobilizer is proposed and fabricated, which comprises a nitrogen-doped hollow MXene sphere (NM) planted with the dual-sided porous carbon network (DCNM). In such a configuration, the highly conductive macroporous NM not only facilitates fast electron transport but also acts as the capturing center to entrap polyphosphide through strong chemical adsorption, while the uniformly distributed micromesoporous carbon network in or out of the sphere provides reliable RP accommodation and alleviates the volume expansion, as well as creates interpenetrating ion diffusion and electron transport channels. Benefiting from the synergistic effect of the triple-shelled architecture and the exclusive restraint, the Hoya-like DCNM@RP anode exhibits significantly enhanced electrochemical performances for LIBs and SIBs, delivering a combination of high reversible capacity, splendid rate properties, and extended cycling performance: up to 1800 cycles with 0.01% per cycle capacity decay for LIBs and 0.024% per cycle over 1000 cycles for SIBs at 2 C.

A study on the changing trend and influencing factors of hospitalization costs of schizophrenia in economically underdeveloped areas of China.

The public health problems caused by schizophrenia are becoming increasingly prominent and can place a huge economic burden on society. This study takes Gansu Province as an example to analyze the level and changing trend of the economic burden of schizophrenia inpatients in economically underdeveloped areas of China. Using a multi-stage stratified cluster sampling method, 39,054 schizophrenics from 197 medical and health institutions in Gansu Province were selected as the research objects, and their medical expenses and related medical records were obtained from the medical information system. The rank sum test and Spearman rank correlation were used for univariate analysis. Quantile regression and random forest were used to analyze the influencing factors. The results show that the average length of stay of schizophrenics in Gansu Province of China was 52.01 days, and the average hospitalization cost was USD1653.96 from 2014 to 2019. During the six years, the average hospitalization costs per time decreased from USD2136.85 to USD1401.33. The average out-of-pocket costs per time decreased from USD1238.78 to USD267.68. And the average daily hospitalization costs increased from USD38.18 to USD41.25. The main factors influencing hospitalization costs are length of stay, proportion of medications, and schizophrenic subtype. The hospitalization costs per time of schizophrenics in Gansu Province have decreased but remain at a high level compared to some other chronic non-communicable diseases. In the future, attention should be paid to improving the efficiency of medical institutions, enhancing community management, and promoting the transformation of the management model of schizophrenia.

Kawasaki disease coronary artery lesions prediction with monocyte-to-high-density lipoprotein ratio.

OBJECTIVE: The aim of this study was to evaluate the predictive value of the monocyte-to-high-density lipoprotein ratio (MHR) in Kawasaki disease (KD) complicated with coronary artery lesions (CALs) and to construct a nomogram prediction model. METHODS: The medical records of KD inpatients diagnosed in the Department of Pediatrics of Lanzhou University Second Hospital from May 2015 to September 2021 were retrospectively analyzed. ROC curves were applied to evaluate the predictive value of MHR in KD complicated with CALs, and logistic regression analysis was used to screen independent risk factors. We constructed a nomogram model and performed internal validation. RESULTS: A total of 568 KD patients were enrolled in the study. MHR was significantly higher in KD patients complicated with CALs and was identified as an independent risk factor for CALs (OR: 1.604, 95% CI: 1.292-1.990). The area under the ROC curve for MHR in predicting CALs was 0.661. The C-index of the nomogram model constructed by incorporating MHR was 0.725 (95% CI: 0.682-0.768), and the calibration curve revealed good agreement between the predicted and actual probabilities. CONCLUSIONS: MHR may not be suitable as a single biomarker to predict the occurrence of CALs, but the nomogram model constructed in combination with other independent risk factors had acceptable predictive performance. IMPACT: The inflammatory response plays an important role in the pathogenesis of Kawasaki disease. The monocyte-to-high-density lipoprotein ratio is a novel systemic inflammation marker. The monocyte-to-high-density lipoprotein ratio is an independent risk factor for Kawasaki disease complicated with coronary artery lesions. The nomogram established by incorporating the monocyte-to-high-density lipoprotein ratio has satisfactory predictive performance for coronary artery lesion formation.

The Liver Tumor Segmentation Benchmark (LiTS).

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018. The image dataset is diverse and contains primary and secondary tumors with varied sizes and appearances with various lesion-to-background levels (hyper-/hypo-dense), created in collaboration with seven hospitals and research institutions. Seventy-five submitted liver and liver tumor segmentation algorithms were trained on a set of 131 computed tomography (CT) volumes and were tested on 70 unseen test images acquired from different patients. We found that not a single algorithm performed best for both liver and liver tumors in the three events. The best liver segmentation algorithm achieved a Dice score of 0.963, whereas, for tumor segmentation, the best algorithms achieved Dices scores of 0.674 (ISBI 2017), 0.702 (MICCAI 2017), and 0.739 (MICCAI 2018). Retrospectively, we performed additional analysis on liver tumor detection and revealed that not all top-performing segmentation algorithms worked well for tumor detection. The best liver tumor detection method achieved a lesion-wise recall of 0.458 (ISBI 2017), 0.515 (MICCAI 2017), and 0.554 (MICCAI 2018), indicating the need for further research. LiTS remains an active benchmark and resource for research, e.g., contributing the liver-related segmentation tasks in http://medicaldecathlon.com/. In addition, both data and online evaluation are accessible via https://competitions.codalab.org/competitions/17094.

Application of nnU-Net for Automatic Segmentation of Lung Lesions on CT Images and Its Implication for Radiomic Models.

Radiomics investigates the predictive role of quantitative parameters calculated from radiological images. In oncology, tumour segmentation constitutes a crucial step of the radiomic workflow. Manual segmentation is time-consuming and prone to inter-observer variability. In this study, a state-of-the-art deep-learning network for automatic segmentation (nnU-Net) was applied to computed tomography images of lung tumour patients, and its impact on the performance of survival radiomic models was assessed. In total, 899 patients were included, from two proprietary and one public datasets. Different network architectures (2D, 3D) were trained and tested on different combinations of the datasets. Automatic segmentations were compared to reference manual segmentations performed by physicians using the DICE similarity coefficient. Subsequently, the accuracy of radiomic models for survival classification based on either manual or automatic segmentations were compared, considering both hand-crafted and deep-learning features. The best agreement between automatic and manual contours (DICE = 0.78 ± 0.12) was achieved averaging 2D and 3D predictions and applying customised post-processing. The accuracy of the survival classifier (ranging between 0.65 and 0.78) was not statistically different when using manual versus automatic contours, both with hand-crafted and deep features. These results support the promising role nnU-Net can play in automatic segmentation, accelerating the radiomic workflow without impairing the models' accuracy. Further investigations on different clinical endpoints and populations are encouraged to confirm and generalise these findings.