Preparation and application of KCC-1@ZIF-8 for the solid extraction of tetracycline with high adsorption capacity.

In this study, three different materials were prepared: dendritic fiber-type silica (KCC-1), zeolitic imidazolate framework-8 (ZIF-8), and a new composite material called KCC-1@ZIF-8. These materials were synthesized using microemulsion, stirring, and coating methods, respectively. The properties of the materials were characterized using various techniques such as transmission electron microscopy (TEM), scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), TGA and X-ray diffraction (XRD). The results showed that KCC-1@ZIF-8 exhibited a significant increase in the BET surface area and pore size compared to the individual components KCC-1 and ZIF-8. These improved properties of the composite material were beneficial for enhancing the adsorption capacity. The effects of initial concentrations, solution pH and reaction time on the adsorption capacity were investigated. The adsorption kinetics and isothermal data of ZIF-8 and KCC-1@ZIF-8 fitted well with pseudo-second-order and Langmuir isotherm models. The results of adsorption thermodynamics show that the adsorption process is spontaneous and endothermic. KCC-1@ZIF-8 exhibited a very high adsorption capacity (751.46 mg g-1) at an initial TC hydrochloride concentration of 80 mg L-1 in an aqueous solution at 301.15 K, and the value was higher than that of ZIF-8 (549.80 mg g-1) under the same conditions. KCC-1 exhibited a relatively lower capacity (37.860 mg g-1). Based on these findings, KCC-1@ZIF-8 was considered a promising adsorbent for the treatment of wastewater contaminated with TC hydrochloride. Additionally, the composite material, when combined with high-performance liquid chromatography (HPLC), could be used as a solid-phase extraction adsorbent for the adsorption of TC hydrochloride in animal foodstuff samples. The calibration curves showed a linear range of 20-500 µg L-1, and the recovery rate ranged from 85.216% to 90.717%. No one has made adsorbents with this new structure before, and KCC-1@ZIF-8 possessed excellent adsorption properties, which make it a potential candidate for environmental remediation and analytical applications involving TC hydrochloride.

Machine learning model predicts airway stenosis requiring clinical intervention in patients after lung transplantation: a retrospective case-controlled study.

BACKGROUND: Patients with airway stenosis (AS) are associated with considerable morbidity and mortality after lung transplantation (LTx). This study aims to develop and validate machine learning (ML) models to predict AS requiring clinical intervention in patients after LTx. METHODS: Patients who underwent LTx between January 2017 and December 2019 were reviewed. The conventional logistic regression (LR) model was fitted by the independent risk factors which were determined by multivariate LR. The optimal ML model was determined based on 7 feature selection methods and 8 ML algorithms. Model performance was assessed by the area under the curve (AUC) and brier score, which were internally validated by the bootstrap method. RESULTS: A total of 381 LTx patients were included, and 40 (10.5%) patients developed AS. Multivariate analysis indicated that male, pulmonary arterial hypertension, and postoperative 6-min walking test were significantly associated with AS (all P < 0.001). The conventional LR model showed performance with an AUC of 0.689 and brier score of 0.091. In total, 56 ML models were developed and the optimal ML model was the model fitted using a random forest algorithm with a determination coefficient feature selection method. The optimal model exhibited the highest AUC and brier score values of 0.760 (95% confidence interval [CI], 0.666-0.864) and 0.085 (95% CI, 0.058-0.117) among all ML models, which was superior to the conventional LR model. CONCLUSIONS: The optimal ML model, which was developed by clinical characteristics, allows for the satisfactory prediction of AS in patients after LTx.

Deep learning system for malignancy risk prediction in cystic renal lesions: a multicenter study.

OBJECTIVES: To develop an interactive, non-invasive artificial intelligence (AI) system for malignancy risk prediction in cystic renal lesions (CRLs). METHODS: In this retrospective, multicenter diagnostic study, we evaluated 715 patients. An interactive geodesic-based 3D segmentation model was created for CRLs segmentation. A CRLs classification model was developed using spatial encoder temporal decoder (SETD) architecture. The classification model combines a 3D-ResNet50 network for extracting spatial features and a gated recurrent unit (GRU) network for decoding temporal features from multi-phase CT images. We assessed the segmentation model using sensitivity (SEN), specificity (SPE), intersection over union (IOU), and dice similarity (Dice) metrics. The classification model's performance was evaluated using the area under the receiver operator characteristic curve (AUC), accuracy score (ACC), and decision curve analysis (DCA). RESULTS: From 2012 to 2023, we included 477 CRLs (median age, 57 [IQR: 48-65]; 173 men) in the training cohort, 226 CRLs (median age, 60 [IQR: 52-69]; 77 men) in the validation cohort, and 239 CRLs (median age, 59 [IQR: 53-69]; 95 men) in the testing cohort (external validation cohort 1, cohort 2, and cohort 3). The segmentation model and SETD classifier exhibited excellent performance in both validation (AUC = 0.973, ACC = 0.916, Dice = 0.847, IOU = 0.743, SEN = 0.840, SPE = 1.000) and testing datasets (AUC = 0.998, ACC = 0.988, Dice = 0.861, IOU = 0.762, SEN = 0.876, SPE = 1.000). CONCLUSION: The AI system demonstrated excellent benign-malignant discriminatory ability across both validation and testing datasets and illustrated improved clinical decision-making utility. CRITICAL RELEVANCE STATEMENT: In this era when incidental CRLs are prevalent, this interactive, non-invasive AI system will facilitate accurate diagnosis of CRLs, reducing excessive follow-up and overtreatment. KEY POINTS: The rising prevalence of CRLs necessitates better malignancy prediction strategies. The AI system demonstrated excellent diagnostic performance in identifying malignant CRL. The AI system illustrated improved clinical decision-making utility.

Effects of temporal changes in resting heart rate on future diabetes-related outcomes.

Background and aims: Most studies have analyzed the relationship between resting heart rate (RHR) measured at only one time point and future clinical events. The current study aims to investigate the impact of long-term RHR changes on future clinical outcomes in a decade-long cohort with type 2 diabetes mellitus (T2DM). Methods: The two-staged follow-up involved 2,513 T2DM participants. The first stage (2008-2014) intended to identify levels and trends in RHR changes, while the second stage (2014-2018) attempted to collect new occurrence records of clinical results. Cox proportional hazards models were applied to predict hazard ratios (HRs), along with 95% confidence interval (CI) for the correlation between RHR changes and future events. Results: There is no significant correlation between baseline RHR levels and long-term clinical events. According to the range of RHR change, compared with the stable RHR group, the adjusted HRs for cardiovascular events and all-cause death in the large increase group were 3.40 (95% CI: 1.33-8.71, p=0.010) and 3.22 (95% CI: 1.07-9.64, p=0.037), respectively. While the adjusted HRs for all-cause death and major adverse cardiac and cerebrovascular events (MACCE) in the moderate decrease group were 0.55 (95% CI: 0.31-0.96, p=0.037) and 0.51 (95% CI: 0.26-0.98, p=0.046). According to the trend of RHR, compared with the normal-normal group, the adjusted HRs for composite endpoint events and cerebrovascular events in the normal-high group were 1.64 (95% CI: 1.00-2.68, p=0.047) and 2.82 (95% CI: 1.03-7.76, p=0.043), respectively. Conclusion: Changes in RHR had predictive value for long-term clinical events in diabetic populations. Individuals with significantly elevated RHR over a particular period of time showed an increased risk of adverse events.

Guilingji capsule for Alzheimer's disease: secondary analysis of a randomized non-inferiority controlled trial.

OBJECTIVE: To investigate the effectiveness and safety of Guilingji capsule (, GLJC) in treatment of Alzheimer's disease (AD) patients with kidney-marrow deficiency pattern (KMDP) compared with gingko extract tablets. METHODS: This is a secondary analysis of a large-scale multicenter randomized non-inferiority clinical trial. A total of 120 AD patients with KMDP were enrolled in this study. The participants were randomly categorized into two groups: (a) GLJC group ( = 60) and (b) gingko group ( = 60). The GLJC group was treated with GLJC and gingko extract mimetic tablets, whereas the gingko group received gingko extract tablets and mimetic GLJC. The data on the Mini-Mental State Examination (MMSE), Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS-Cog), Activities of Daily Living (ADL), and Chinese Medicine Symptom Scale (CM-SS) was evaluated at 0, 12, and 24 weeks of treatment. The serum levels of acetylcholine (Ach), acetylcholinesterase (AchE), B-cell lymphoma-2 (Bcl-2), and Bcl-2-associated X protein (Bax) in the participants were measured before and after 24 weeks of treatment. The safety was based on the incidence of adverse events. RESULTS: Both interventions significantly increased the MMSE scores of the participants and decreased their ADAS-Cog, ADL, and CM-SS scores ( < 0.01). Compared with the gingko group, the GLJC group had a higher effective rate of improvement in the symptoms of "amnesia" and "dull expression and slow thinking" at the 12th week and 24th week ( < 0.05, < 0.01). In the GLJC group, serum Bcl-2 levels were significantly increased at the 24th week ( < 0.05). Serum Bax and AchE levels of the two groups were significantly decreased at the 24th week ( < 0.01). No treatment-related adverse events were reported in the two groups. CONCLUSIONS: GLJC is equivalent to the gingko extract tablets in terms of improving cognitive function and the quality of life in AD patients with KMDP and has good clinical efficacy and safety. When it comes to improving TCM symptoms and anti-aging, GLJC is even more advantageous.

Effects of early mental state changes on physical functions in elderly patients with a history of falls.

BACKGROUND: Fear of falling is a potential consequence for older adults who have experienced a fall. Whether such psychological concerns related to falls, in turn, affect physical function? Especially those who have a history of falling but have not been diagnosed with anxiety, depression, or both. This study aimed to clarify the effects of early psychological changes on the physical function of older patients. METHODS: The 111 participants with falling history were divided into the poor physical function (PPF) group with the Short Physical Performance Battery (SPPB) ≤ 9 and the good physical function (GPF) group with SPPB > 9. Their physical function was assessed through 4-m gait speed (4MGS), five times sit-to-stand test (FTSST), grip strength, and Timed Up and Go tests TUGT. Their mental state was assessed by the self-rating anxiety/depression scale (SAS/SDS). RESULTS: (1) SAS/SDS scores were negatively correlated with the SPPB score, gait speed, and maximum grip strength (males). (2) Multivariate logistic regression analysis showed that the SPPB score was subject to such independent influence factors: cerebrovascular disease (OR = 11.805; P = 0.005), normal ratio of grip strength (OR = 0.046; P = 0.016), TUGT (OR = 1.717; P < 0.001), and SDS score (OR = 1.154; P = 0.008). (3) The area under the ROC curve was 0.699 (0.601, 0.797) for SAS score, with a sensitivity of 0.776 and a specificity of 0.547; the AUC was 0.694 (0.596, 0.792) for SDS score, with a sensitivity of 0.586 and a specificity of 0.755. CONCLUSIONS: In older adults with a history of falls without a diagnosis of anxiety or depression, higher SAS/SDS scores were associated with worse fall-related physical function, and there was a statistically significant correlation between the two. This may indicate a risk of falling again in the future.

Comparison of oral sodium phosphate tablets and polyethylene glycol lavage solution for colonoscopy preparation: a systematic review and meta-analysis of randomized clinical trials.

Objective: To systematically compare the bowel cleaning ability, patient tolerance and safety of oral sodium phosphate tablets (NaPTab) and oral polyethylene glycol electrolyte lavage solution (PEGL) to inform clinical decision making. Methods: PubMed, Embase, CBM, WanFang Data, CNKI, and VIP databases were searched for studies that used randomized controlled trials (RCTs) to compare the roles of NaPTab and PEGL in bowel preparation before colonoscopy. Two reviewers independently screened the studies, extracted data, and assessed the risk of bias in the included papers. A meta-analysis was performed using RevMan 5.3 software. Results: A total of 13 RCTs were eligible for inclusion, including 2,773 patients (1,378 and 1,395 cases in the NaPTab and PEGL groups, respectively). Meta-analysis revealed no significant difference in the cleansing quality of the NaPTab and PEGL groups [RR 1.02, 95% CI (0.96-1.08), P = 0.46]. The incidence of nausea was lower in the NaPTab group than in the PEGL group [RR 0.67, 95% CI (0.58-0.76), p < 0.00001]. Patients rated the taste of NaPTab higher than PEGL [RR 1.33, 95% CI (1.26-1.40), P < 0.00001]. Willingness to repeat the treatment was also higher in the NaPTab group than in the PEGL group [RR 1.52, 95% CI (1.28-1.80), P < 0.00001]. Both serum potassium and serum calcium decreased in both groups after the preparation; however, meta-analysis revealed that both minerals decreased more in the NaPTab group than in the PEGL group [MD = 0.38, 95% CI (0.13-0.62), P = 0.006 for serum potassium and MD = 0.41, 95% CI (0.04-0.77), P = 0.03 for serum calcium]. Meanwhile, serum phosphorus increased in both groups after the preparation; however, levels increased more in the NaPTab group than in the PEGL group [MD 4.51, (95% CI 2.9-6.11), P < 0.00001]. Conclusions: While NaP tablets and PEGL were shown to have a similar cleaning effect before colonoscopy, NaP tablets had improved patient tolerance. However, NaP tablets had a strong effect on serum potassium, calcium, and phosphorus levels. For patients with low potassium, low calcium, and renal insufficiency, NaP tablets should be prescribed with caution. For those at high-risk for acute phosphate nephropathy, NaP tablets should be avoided. Given the low number and quality of included studies, these conclusions will require additional verification by large high-quality studies. Systematic review registration: 10.37766/inplasy2023.5.0013, identifier: NPLASY202350013.

Trends in the distribution of socioeconomic inequalities in smoking and cessation: evidence among adults aged 18 ~ 59 from China Family Panel Studies data.

INTRODUCTION: Cigarette smoking is usually more prevalent among those with a lower socioeconomic status (SES), which can be driven by inequalities in the initiation and cessation of smoking, giving rise to SES disparities in health. This study aimed to gauge the SES inequalities in smoking related behaviours and their evolving trends based on a nationally representative database. METHOD: Data were extracted from repeated cross-sectional China Family Panel Studies (CFPS) of adults aged ≥18 and <60 years in 2012, 2014, 2016 and 2018. SES was constructed by principal component analysis based on income, education and occupation. Regression-based odds ratios and coefficients as the relative effect index of inequality were applied to quantify the degree of socioeconomic inequality in smoking related behaviours and to adjust for possible confounding factors. Multivariable regressions were utilized to explore the temporal trends in smoking inequalities. RESULTS: The smoking prevalence among men decreased from 61.16% to 2012 to 57.88% in 2018, cigarette consumption among current smokers declined from 16.71 to 15.49 cigs/per day, and the cessation rate increased from 17.55% to 24.08%. Cigarette consumption for women decreased from 13.39 in 2012 to 11.01 cigs/per day in 2018. Smoking prevalence showed significant SES inequalities among men and women from 2012 to 2018 (men: OR2012 (95%CI)= 0.72 (0.63, 0.83), OR2014 = 0.60 (0.52, 0.69), OR2016 = 0.58 (0.50, 0.67), OR2018 = 0.56 (0.48, 0.66); women: OR2012 = 0.63 (0.41, 0.97), OR2014 = 0.50 (0.32, 0.79), OR2016 = 0.44 (0.26,  0.73), OR2018 = 0.50 (0.30,  0.85)). Cigarette consumption showed significant SES inequalities among men from 2012 to 2018 (ß2012=-1.39 (-2.22, -0.57), ß2014=-2.37 (-3.23, -1.50), ß2016=-2.35 (-3.25, -1.44), ß2018=-2.91 (-3.86, -1.97)). In 2018, inequality emerged in smoking cessation rates among men and smoking intensity among women. However, all tests for trends in changes over time were not statistically significant (P varied from 0.072 to 0.602). CONCLUSION: The smoking prevalence declined between 2012 and 2018 in China. However, SES inequalities in smoking persist, while socioeconomic inequalities in smoking were not alleviated among adults aged 18 ~ 59 in China. Tobacco control measures should be implemented by giving more attention to people with lower SES who are more vulnerable to tobacco use.

Machine Learning-Based Prognostic Model for Patients After Lung Transplantation.

Importance: Although numerous prognostic factors have been found for patients after lung transplantation (LTx) over the years, an accurate prognostic tool for LTx recipients remains unavailable. Objective: To develop and validate a prognostic model for predicting overall survival in patients after LTx using random survival forests (RSF), a machine learning algorithm. Design, Setting, and Participants: This retrospective prognostic study included patients who underwent LTx between January 2017 and December 2020. The LTx recipients were randomly assigned to training and test sets in accordance with a ratio of 7:3. Feature selection was performed using variable importance with bootstrapping resampling. The prognostic model was fitted using the RSF algorithm, and a Cox regression model was set as a benchmark. The integrated area under the curve (iAUC) and integrated Brier score (iBS) were applied to assess model performance in the test set. Data were analyzed from January 2017 to December 2019. Main Outcomes And Measures: Overall survival in patients after LTx. Results: A total of 504 patients were eligible for this study, consisting of 353 patients in the training set (mean [SD] age, 55.03 [12.78] years; 235 [66.6%] male patients) and 151 patients in the test set (mean [SD] age, 56.79 [10.95] years; 99 [65.6%] male patients). According to the variable importance of each factor, 16 were selected for the final RSF model, and postoperative extracorporeal membrane oxygenation time was identified as the most valuable factor. The RSF model had excellent performance with an iAUC of 0.879 (95% CI, 0.832-0.921) and an iBS of 0.130 (95% CI, 0.106-0.154). The Cox regression model fitted by the same modeling factors to the RSF model was significantly inferior to the RSF model with an iAUC of 0.658 (95% CI, 0.572-0.747; P < .001) and an iBS of 0.205 (95% CI, 0.176-0.233; P < .001). According to the RSF model predictions, the patients after LTx were stratified into 2 prognostic groups displaying significant difference, with mean overall survival of 52.91 months (95% CI, 48.51-57.32) and 14.83 months (95% CI, 9.44-20.22; log-rank P < .001), respectively. Conclusions and relevance: In this prognostic study, the findings first demonstrated that RSF could provide more accurate overall survival prediction and remarkable prognostic stratification than the Cox regression model for patients after LTx.

Long-term exposure to arsenic in drinking water leads to myocardial damage by oxidative stress and reduction in NO.

Chronic arsenic exposure causes myocardial damage. The aim of this study is to investigate if oxidative stress and reduction in NO is involved in the myocardial damage induced by arsenic in drinking water. Rats were divided into a control group and different doses of sodium arsenite. With increasing sodium arsenite concentrations in drinking water, localised inflammatory foci and necrotic myocardial tissues were gradually observed. Compared to the control group, the activities and gene expression of antioxidant enzymes in arsenic-exposed rats decreased. NO content and the NOS activity as well as the expression of NOS mRNA in the myocardial tissue of exposed rats, decreased, and the extracellular NO content of cardiomyocytes treated with sodium arsenite also decreased. The rate of cell apoptosis induced by sodium arsenite decreased after treatment with sodium nitroprusside (an NO donor). In conclusion, arsenic exposure in drinking water can lead to myocardial injury and cardiomyocyte apoptosis through oxidative stress and a reduction in NO content.

In vivo cadmium-assisted dilute acid pretreatment of the phytoremediation sweet sorghum for enzymatic hydrolysis and cadmium enrichment.

Phytoremediation with energy crops is considered an integrated technology that provides both environment and energy benefits. Herein, the sweet sorghum cultivated on Cd-contaminated farmland (1.21 mg/kg of Cd in the soil) showed promising phytoremediation potential, and the approach for utilizing sorghum stalks was explored. Sweet sorghum bagasse with Cd contamination was pretreated with dilute acid in order to improve enzymatic saccharification and achieve Cd recovery, resulting in harmless and value-added utilization. After pretreatment, hemicelluloses were dramatically degraded, and the lignocellulosic structures were partially deconstructed with xylan removal up to 98.1%. Under the optimal condition (0.75% H2SO4), the highest total sugar yield was 0.48 g/g of raw bagasse; and nearly 98% of Cd was enriched in the liquid phase. Compared with normal biomass, Cd reduced the biomass recalcitrance and further facilitated the deconstruction of biomass under super dilute acid conditions. This work provided an example for the subsequent valorization of Cd-containing biomass and Cd recovery, which will greatly facilitate the development of phytoremediation of heavy metal contaminated soil.

Deep learning and radiomic feature-based blending ensemble classifier for malignancy risk prediction in cystic renal lesions.

BACKGROUND: The rising prevalence of cystic renal lesions (CRLs) detected by computed tomography necessitates better identification of the malignant cystic renal neoplasms since a significant majority of CRLs are benign renal cysts. Using arterial phase CT scans combined with pathology diagnosis results, a fusion feature-based blending ensemble machine learning model was created to identify malignant renal neoplasms from cystic renal lesions (CRLs). Histopathology results were adopted as diagnosis standard. Pretrained 3D-ResNet50 network was selected for non-handcrafted features extraction and pyradiomics toolbox was selected for handcrafted features extraction. Tenfold cross validated least absolute shrinkage and selection operator regression methods were selected to identify the most discriminative candidate features in the development cohort. Feature's reproducibility was evaluated by intra-class correlation coefficients and inter-class correlation coefficients. Pearson correlation coefficients for normal distribution and Spearman's rank correlation coefficients for non-normal distribution were utilized to remove redundant features. After that, a blending ensemble machine learning model were developed in training cohort. Area under the receiver operator characteristic curve (AUC), accuracy score (ACC), and decision curve analysis (DCA) were employed to evaluate the performance of the final model in testing cohort. RESULTS: The fusion feature-based machine learning algorithm demonstrated excellent diagnostic performance in external validation dataset (AUC = 0.934, ACC = 0.905). Net benefits presented by DCA are higher than Bosniak-2019 version classification for stratifying patients with CRL to the appropriate surgery procedure. CONCLUSIONS: Fusion feature-based classifier accurately distinguished malignant and benign CRLs which outperformed the Bosniak-2019 version classification and illustrated improved clinical decision-making utility.

Effects of breviscapine on cerebral ischemia-reperfusion injury and intestinal flora imbalance by regulating the TLR4/MyD88/NF-κB signaling pathway in rats.

ETHNOPHARMACOLOGICAL RELEVANCE: The plant Erigeron breviscapus (Vant.) Hand.-Mazz.,a Chinese herbal medicine with multiple pharmacological effects and clinical applications, has been traditionally used in the treatment of paralysis caused by stroke and joint pain from rheumatism by the Yi minority people of Southwest China for generations.However, its mechanism involves many factors and has not been fully clarified. AIM OF THE STUDY: Taking intestinal flora as the target, the protective effect of extract(breviscapine) of E. breviscapus on cerebral ischemia and its possible mechanism were discussed from the perspective of brain inflammatory pathway and intestinal CYP3A4, which depends on intestinal flora. MATERIALS AND METHODS: In this study, we first verified the binding ability between major active ingredient of Erigeron breviscapus and the core target TLR4 protein by molecular docking using Vina software.We established a rat model of cerebral ischemia-reperfusion injury in vivo.The neurological function of rats was scored by Bederson score table, the cerebral infarction volume was detected by TTC staining, and the serum NSE level was detected by ELASA. 16S rRNA sequencing was used to detect the intestinal flora of rats in each group.The expression levels of cerebral TLR4/MyD88/NF-κB and CYP3A4 mRNA and protein in different intestinal segments were detected by qRT-PCR and Western blot. RESULTS: Compared with the model group, the neurological injury score, infarct volume and serum NSE concentration of breviscapine low, medium and high dose groups and nimodipine groups decreased significantly. Meanwhile, breviscapine could significantly reduce the expression level of the TLR4/MyD88/NF-κB in brain tissue and CYP3A4 in different intestinal segments of rats with cerebral ischemia-reperfusion injury. In addition, breviscapine also significantly ameliorated intestinal flora dysbiosis of rats with cerebral ischemia-reperfusion injury. CONCLUSIONS: Breviscapine can protect rats from cerebral ischemia-reperfusion injury by regulating intestinal flora, inhibiting brain TLR4/MyD88/NF-κB inflammatory pathway and intestinal CYP3A4 expression.

Laponite intercalated biomimetic multilayer coating prevents glucocorticoids induced orthopedic implant failure.

Implant failure, which is commonly associated with failure of osseointegration and peri-implant infection, is a severe complication of orthopedic surgery. In particular, the survival rate of implants is significantly decreased in patients using long-term glucocorticoids (GCs). However, the exact molecular mechanism underlying GCs-induced implant loosening, as well as preventive strategies for these patients, is unclear. To address this problem, we performed RNA-sequencing and found that WNT16 was correlated with GCs-induced osteopenia (LogFC = -5.17, p ＜ 0.01). Inspired by the concept of "organic-inorganic" hybrid, we theorized to introduce a bioactive two-dimensional nanosheet into a layer-by-layer (LbL) self-assembly coating to construct a customized implant that targets WNT16. After screening commercially available nanosheets, laponite (LAP) was identified as a cost-effective rescuer for GCs-induced WNT16 inhibition, which was then intercalated into LbL deposition system consisting of quaternized chitosan (QCS) and hyaluronic acid (HA). The hybrid coating (QCS/HA/LAP) showed micrometer thickness and improved hydrophilicity and interface roughness. Furthermore, QCS/HA/LAP coated polyetheretherketone (PEEK) implant enhanced cell viability, adhesion, and osteogenic differentiation of bone marrow mesenchymal stem cells (BMSCs), and promoted osteointegration of PEEK in GCs-treated rats by targeting the WNT16/ß-catenin axis. The assembled QCS has proven antibacterial properties, and the hybrid coating exerted potent detrimental effects against methicillin-resistant Staphylococcus aureus (MRSA) and Escherichia coli (E. coli), both in vitro and in vivo. Taken together, these results suggest that QCS/HA/LAP coating has great potential for use in implants customization, and has synergistic pro-osteogenic and antibacterial effects that help prevent implant failure in GCs-treated patients.

Decellularized tilapia fish skin: A novel candidate for tendon tissue engineering.

The poor regenerative ability of injured tendon tissues remains a clinical challenge. However, decellularized extracellular matrix (ECM) combined with stem cells shows promise. In contrast to bovine and porcine ECM, marine-derived decellularized ECM has several advantages; it is easily obtained, poses less biological risk, and is not contraindicated on religious grounds. This study successfully fabricated decellularized tilapia fish skin (DTFS) with copious preserved collagen fibers and natural pore structures. The outer layer is smooth and dense, while the inner layer has a soft structure with a rough surface. After crosslinking with 1-ethyl-3-(3-dimethylaminopropyl)-carbodiimide (EDC) and N-hydroxysuccinimide (NHS), crosslinked DTFS (C-DTFS) showed improved mechanics in dry and wet conditions. In vitro, the leach liquor of crosslinked DTFS showed no cytotoxicity and promoted migration and tenonic differentiation of tendon-derived stem cells (TDSCs). Meanwhile, TDSCs seeded in the inner surface of DTFS maintained viability, differentiated, and exhibited spreading. Furthermore, cell-seeded scaffolds guided the regeneration of tendon tissue in a rat Achilles tendon defect model. Our results suggest that DTFS combined with TDSCs is a novel and promising therapeutic option for tendon tissue engineering.

Stratification of malignant renal neoplasms from cystic renal lesions using deep learning and radiomics features based on a stacking ensemble CT machine learning algorithm.

Using nephrographic phase CT images combined with pathology diagnosis, we aim to develop and validate a fusion feature-based stacking ensemble machine learning model to distinguish malignant renal neoplasms from cystic renal lesions (CRLs). This retrospective research includes 166 individuals with CRLs for model training and 47 individuals with CRLs in another institution for model testing. Histopathology results are adopted as diagnosis criterion. Nephrographic phase CT scans are selected to build the fusion feature-based machine learning algorithms. The pretrained 3D-ResNet50 CNN model and radiomics methods are selected to extract deep features and radiomics features, respectively. Fivefold cross-validated least absolute shrinkage and selection operator (LASSO) regression methods are adopted to identify the most discriminative candidate features in the development cohort. Intraclass correlation coefficients and interclass correlation coefficients are employed to evaluate feature's reproducibility. Pearson correlation coefficients for normal distribution features and Spearman's rank correlation coefficients for non-normal distribution features are used to eliminate redundant features. After that, stacking ensemble machine learning models are developed in the training cohort. The area under the receiver operator characteristic curve (ROC), calibration curve, and decision curve analysis (DCA) are adopted in the testing cohort to evaluate the performance of each model. The stacking ensemble machine learning algorithm reached excellent diagnostic performance in the testing dataset. The calibration plot shows good stability when using the stacking ensemble model. Net benefits presented by DCA are higher than the Bosniak 2019 version classification when employing any machine learning algorithm. The fusion feature-based machine learning algorithm accurately distinguishes malignant renal neoplasms from CRLs, which outperformed the Bosniak 2019 version classification, and proves to be more applicable for clinical decision-making.

Effectiveness of Mobile Medical Apps in Ensuring Medication Safety Among Patients With Chronic Diseases: Systematic Review and Meta-analysis.

BACKGROUND: Along with the rapid growth of the global aging society, the mobile and health digital market has expanded greatly. Countless mobile medical apps (mmApps) have sprung up in the internet market, aiming to help patients with chronic diseases achieve medication safety. OBJECTIVE: Based on the medication safety action plans proposed by the World Health Organization, we aimed to explore the effectiveness of mmApps in ensuring the medication safety of patients with chronic diseases, including whether mmApps can improve the willingness to report adverse drug events (ADEs), improve patients' medication adherence, and reduce medication errors. We hoped to verify our hypothesis through a systematic review and meta-analysis. METHODS: The meta-analysis was performed in strict accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and included literature searched from 7 databases-PubMed, Web Of Science, Embase, CINAHL, China National Knowledge Infrastructure, Wanfang, and SinoMed. The publication time was limited to the time of database establishment to April 30, 2022. Studies were screened based on inclusion and exclusion criteria. The data extracted included authors, years of publication, countries or regions, participants' characteristics, intervention groups, and control groups, among others. Our quality assessment followed the guidelines of the Cochrane Handbook for Systematic Reviews of Interventions, Version 6.3. RevMan 5.2 software (Cochrane Collaboration) was used to analyze the statistical data, and a sensitivity analysis was performed to assess data stability. The degree of stability was calculated by using a different statistical method and excluding large-sample studies from the analysis. RESULTS: We included 8 studies from 5 countries (China, the United States, France, Canada, and Spain) that were published from January 1, 2014, to December 31, 2021. The total number of participants was 1355, and we analyzed the characteristics of included studies, each app's features, the risk of bias, and quality. The results showed that mmApps could increase ADE reporting willingness (relative risk [RR] 2.59, 95% CI 1.26-5.30; P=.009) and significantly improve medication adherence (RR 1.17, 95% CI 1.04-1.31; P=.007), but they had little effect on reducing medication errors (RR 1.54, 95% CI 0.33-7.29; P=.58). CONCLUSIONS: We analyzed the following three merits of mmApps, with regard to facilitating the willingness to report ADEs: mmApps facilitate more communication between patients and physicians, patients attach more importance to ADE reporting, and the processing of results is transparent. The use of mmApps improved medication adherence among patients with chronic diseases by conveying medical solutions, providing educational support, tracking medications, and allowing for remote consultations. Finally, we found 3 potential reasons for why our medication error results differed from those of other studies. TRIAL REGISTRATION: PROSPERO International Prospective Register of Systematic Reviews CRD42022322072; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=322072.

Lensless computational imaging with a hybrid framework of holographic propagation and deep learning.

Lensless imaging has attracted attention as it avoids the bulky optical lens. Lensless holographic imaging is a type of a lensless imaging technique. Recently, deep learning has also shown tremendous potential in lensless holographic imaging. A labeled complex field including real and imaginary components of the samples is usually used as a training dataset. However, obtaining such a holographic dataset is challenging. In this Letter, we propose a lensless computational imaging technique with a hybrid framework of holographic propagation and deep learning. The proposed framework takes recorded holograms as input instead of complex fields, and compares the input and regenerated holograms. Compared to previous supervised learning schemes with a labeled complex field, our method does not require this supervision. Furthermore, we use the generative adversarial network to constrain the proposed framework and tackle the trivial solution. We demonstrate high-quality reconstruction with the proposed framework compared to previous deep learning methods.

Compared with Him or Her, I Am Not Good Enough: How to Alleviate Depression Due to Upward Social Comparison?

The present study primarily aims to examine the mediating role of core self-evaluation and the moderating role of personal growth initiative (PGI) in the relationship between upward social comparison and senior high school students' depression and to explore the gender difference in this association. A total of 721 Chinese senior high school students (61.16% boys; M = 16.21 years, SD = 1.07) completed measures of upward social comparison, core self-evaluation, personal growth initiative, and depression. Results showed a significant positive correlation between upward social comparison and depression. Core self-evaluation partially mediated this association, and a gender difference in the indirect effect was revealed; that is, the effect was shown to be stronger for girls than for boys. Moreover, for boys, the mediating effect of core self-evaluation was moderated by PGI, and the effect was more potent for individuals with high PGI than for those with low PGI. However, no moderating effect of PGI on the indirect effect was found for girls. These findings suggest that prevention and interventions focusing on reducing upward social comparison and enhancing core self-evaluation may alleviate adolescents' depression symptoms and that more attention should be given to girls with low core self-evaluation and boys with high PGI.

Depressive symptoms and gender differences in older adults in Hong Kong during the COVID-19 pandemic: a network analysis approach.

Background: The 2019 novel coronavirus disease (COVID-19) outbreak had a detrimental impact on the mental health of older adults. This study evaluated the central symptoms and their associations in the network of depressive symptoms and compared the network structure differences between male and female older adults in Hong Kong. Methods: Altogether, 3,946 older adults participated in this study. We evaluated the centrality indicators for network robustness using stability and accuracy tests, and examined the potential differences between the structure and connectivity of depression networks in male and female older adults. Results: The overall prevalence of depressive symptoms was 43.7% (95% CI=40.6-46.7%) in males, and 54.8% (95% CI=53.1-56.5%) in females (P<0.05). Sad Mood, Guilt, Motor problems and Lack of Energy were influential symptoms in the network model. Gender differences were found in the network global strength, especially in the following edges: Sad Mood--Guilt, Concentration--Guilt, Anhedonia--Motor, Lack of Energy--Suicide, Appetite--Suicide and Concentration--Suicide. Conclusions: Central symptoms in the depressive symptom network among male and female older adults may be prioritized in the treatment and prevention of depression during the pandemic.