Determining individual suitability for neoadjuvant systemic therapy in breast cancer patients through deep learning.

BACKGROUND: The survival advantage of neoadjuvant systemic therapy (NST) for breast cancer patients remains controversial, especially when considering the heterogeneous characteristics of individual patients. OBJECTIVE: To discern the variability in responses to breast cancer treatment at the individual level and propose personalized treatment recommendations utilizing deep learning (DL). METHODS: Six models were developed to offer individualized treatment suggestions. Outcomes for patients whose actual treatments aligned with model recommendations were compared to those whose did not. The influence of certain baseline features of patients on NST selection was visualized and quantified by multivariate logistic regression and Poisson regression analyses. RESULTS: Our study included 94,487 female breast cancer patients. The Balanced Individual Treatment Effect for Survival data (BITES) model outperformed other models in performance, showing a statistically significant protective effect with inverse probability treatment weighting (IPTW)-adjusted baseline features [IPTW-adjusted hazard ratio: 0.51, 95% confidence interval (CI), 0.41-0.64; IPTW-adjusted risk difference: 21.46, 95% CI 18.90-24.01; IPTW-adjusted difference in restricted mean survival time: 21.51, 95% CI 19.37-23.80]. Adherence to BITES recommendations is associated with reduced breast cancer mortality and fewer adverse effects. BITES suggests that patients with TNM stage IIB, IIIB, triple-negative subtype, a higher number of positive axillary lymph nodes, and larger tumors are most likely to benefit from NST. CONCLUSIONS: Our results demonstrated the potential of BITES to aid in clinical treatment decisions and offer quantitative treatment insights. In our further research, these models should be validated in clinical settings and additional patient features as well as outcome measures should be studied in depth.

US-guided Percutaneous Radiofrequency Ablation for Secondary Hyperparathyroidism: Long-term Outcomes and Prognostic Factors.

Background Although favorable outcomes have been reported with radiofrequency ablation (RFA) for secondary hyperparathyroidism (SHPT), the long-term efficacy remains insufficiently investigated. Purpose To evaluate the long-term efficacy and safety of US-guided percutaneous RFA in patients with SHPT undergoing dialysis and to identify possible predictors associated with treatment failure. Materials and Methods This retrospective study included consecutive patients with SHPT with at least one enlarged parathyroid gland accessible for RFA who were undergoing dialysis at seven tertiary centers from May 2013 to July 2022. The primary end point was the proportion of patients with parathyroid hormone (PTH) levels less than or equal to 585 pg/mL at the end of follow-up. Secondary end points were the proportion of patients with normal calcium and phosphorus levels, the technical success rate, procedure-related complications, and improvement in self-rated hyperparathyroidism-related symptoms (0-3 ranking scale). The Wilcoxon signed rank test and generalized estimating equation model were used to evaluate treatment outcomes. Univariable and multivariable regression analyses identified variables associated with treatment failure (recurrent or persistent hyperparathyroidism). Results This study included 165 patients (median age, 51 years [IQR, 44-60 years]; 92 female) and 582 glands. RFA effectively reduced PTH, calcium, and phosphorus levels, with targeted ranges achieved in 78.2% (129 of 165), 72.7% (120 of 165), and 60.0% (99 of 165) of patients, respectively, at the end of follow-up (mean, 51 months). For the RFA sessions, the technical success rate was 100% (214 of 214). Median symptom scores (ostealgia, arthralgia, pruritus) decreased (all P < .001). Regarding complications, only hypocalcemia (45.8%, 98 of 214) was common. Treatment failure occurred in 36 patients (recurrent [n = 5] or persistent [n = 31] hyperparathyroidism). The only potential independent predictor of treatment failure was having less than four treated glands (odds ratio, 17.18; 95% CI: 4.34, 67.95; P < .001). Conclusion US-guided percutaneous RFA was effective and safe in the long term as a nonsurgical alternative for patients with SHPT undergoing dialysis; the only potential independent predictor of treatment failure was a lower number (<4) of treated glands. © RSNA, 2024 Supplemental material is available for this article.

Optimizing adjuvant treatment options for patients with glioblastoma.

Background: This study focused on minimizing the costs and toxic effects associated with unnecessary chemotherapy. We sought to optimize the adjuvant therapy strategy, choosing between radiotherapy (RT) and chemoradiotherapy (CRT), for patients based on their specific characteristics. This selection process utilized an innovative deep learning method. Methods: We trained six machine learning (ML) models to advise on the most suitable treatment for glioblastoma (GBM) patients. To assess the protective efficacy of these ML models, we employed various metrics: hazards ratio (HR), inverse probability treatment weighting (IPTW)-adjusted HR (HRa), the difference in restricted mean survival time (dRMST), and the number needed to treat (NNT). Results: The Balanced Individual Treatment Effect for Survival data (BITES) model emerged as the most effective, demonstrating significant protective benefits (HR: 0.53, 95% CI, 0.48-0.60; IPTW-adjusted HR: 0.65, 95% CI, 0.55-0.78; dRMST: 7.92, 95% CI, 7.81-8.15; NNT: 1.67, 95% CI, 1.24-2.41). Patients whose treatment aligned with BITES recommendations exhibited notably better survival rates compared to those who received different treatments, both before and after IPTW adjustment. In the CRT-recommended group, a significant survival advantage was observed when choosing CRT over RT (p < 0.001). However, this was not the case in the RT-recommended group (p = 0.06). Males, older patients, and those whose tumor invasion is confined to the ventricular system were more frequently advised to undergo RT. Conclusion: Our study suggests that BITES can effectively identify GBM patients likely to benefit from CRT. These ML models show promise in transforming the complex heterogeneity of real-world clinical practice into precise, personalized treatment recommendations.

Deep learning-guided adjuvant chemotherapy selection for elderly patients with breast cancer.

PURPOSE: The efficacy of adjuvant chemotherapy in elderly breast cancer patients is currently controversial. This study aims to provide personalized adjuvant chemotherapy recommendations using deep learning (DL). METHODS: Six models with various causal inference approaches were trained to make individualized chemotherapy recommendations. Patients who received actual treatment recommended by DL models were compared with those who did not. Inverse probability treatment weighting (IPTW) was used to reduce bias. Linear regression, IPTW-adjusted risk difference (RD), and SurvSHAP(t) were used to interpret the best model. RESULTS: A total of 5352 elderly breast cancer patients were included. The median (interquartile range) follow-up time was 52 (30-80) months. Among all models, the balanced individual treatment effect for survival data (BITES) performed best. Treatment according to following BITES recommendations was associated with survival benefit, with a multivariate hazard ratio (HR) of 0.78 (95% confidence interval (CI): 0.64-0.94), IPTW-adjusted HR of 0.74 (95% CI: 0.59-0.93), RD of 12.40% (95% CI: 8.01-16.90%), IPTW-adjusted RD of 11.50% (95% CI: 7.16-15.80%), difference in restricted mean survival time (dRMST) of 12.44 (95% CI: 8.28-16.60) months, IPTW-adjusted dRMST of 7.81 (95% CI: 2.93-11.93) months, and p value of the IPTW-adjusted Log-rank test of 0.033. By interpreting BITES, the debiased impact of patient characteristics on adjuvant chemotherapy was quantified, which mainly included breast cancer subtype, tumor size, number of positive lymph nodes, TNM stages, histological grades, and surgical type. CONCLUSION: Our results emphasize the potential of DL models in guiding adjuvant chemotherapy decisions for elderly breast cancer patients.

Single-cell transcriptomics reveals variations in monocytes and Tregs between gout flare and remission.

Gout commonly manifests as a painful, self-limiting inflammatory arthritis. Nevertheless, the understanding of the inflammatory and immune responses underlying gout flares and remission remains ambiguous. Here, based on single-cell RNA-Seq and an independent validation cohort, we identified the potential mechanism of gout flare, which likely involves the upregulation of HLA-DQA1+ nonclassical monocytes and is related to antigen processing and presentation. Furthermore, Tregs also play an essential role in the suppressive capacity during gout remission. Cell communication analysis suggested the existence of altered crosstalk between monocytes and other T cell types, such as Tregs. Moreover, we observed the systemic upregulation of inflammatory and cytokine genes, primarily in classical monocytes, during gout flares. All monocyte subtypes showed increased arachidonic acid metabolic activity along with upregulation of prostaglandin-endoperoxide synthase 2 (PTGS2). We also detected a decrease in blood arachidonic acid and an increase in leukotriene B4 levels during gout flares. In summary, our study illustrates the distinctive immune cell responses and systemic inflammation patterns that characterize the transition from gout flares to remission, and it suggests that blood monocyte subtypes and Tregs are potential intervention targets for preventing recurrent gout attacks and progression.

Effective early strategy to prevent olfactory and gustatory dysfunction in COVID-19: A randomized controlled trial.

BACKGROUND: Olfactory and gustatory dysfunctions (OGDs) are key symptoms of COVID-19, which may lead to neurological complications, and lack of effective treatment. This may be because post-disease treatments may be too late to protect the olfactory and gustatory functions. AIM: To evaluate the effectiveness of early use of saline nasal irrigation (SNI), corticosteroid nasal spray, and saline or chlorhexidine gluconate mouthwash for preventing OGDs in COVID-19. DESIGN: This study was a double-blind randomized controlled trial. METHODS: The study was conducted from May 5 to June 16, 2022. We recruited patients from three hospitals who were admitted with COVID-19 but without OGDs on the day of admission. Olfactory and gustatory functions were evaluated using the Taste and Smell Survey and the numerical visual analog scale. Participants were randomized to the saline, drug, or control groups. The control group received no intervention, saline group received SNI plus saline nasal spray and mouthwash, and the trial group received SNI plus budesonide nasal spray and chlorhexidine gluconate mouthwash. Participants were assessed again on the day of discharge. RESULTS: A total of 379 patients completed the trial. The prevalence of OGDs was significantly lower in the saline (11.8%, 95% CI, 6.6-19.0%; P < 0.001) and trial (8.3%, 95% CI, 4.1-14.8%; P < 0.001) groups than in the control group (40.0%, 95% CI, 31.8-48.6%). Additionally, both interventions reduced the severity of OGDs. CONCLUSIONS: We demonstrated effective strategies for preventing COVID-19-related OGDs, and the findings may guide early management of SARS-CoV-2 infection to reduce the incidence of COVID-19-related complications.

MSU crystal deposition contributes to inflammation and immune responses in gout remission.

As a prominent feature of gout, monosodium urate (MSU) crystal deposition induces gout flares, but its impact on immune inflammation in gout remission remains unclear. Using single-cell RNA sequencing (scRNA-seq), we characterize the transcription profiling of peripheral blood mononuclear cells (PBMCs) among intercritical remission gout, advanced remission gout, and normal controls. We find systemic inflammation in gout remission with MSU crystal deposition at the intercritical and advanced stages, evidenced by activated inflammatory pathways, strengthened inflammatory cell-cell interactions, and elevated arachidonic acid metabolic activity. We also find increased HLA-DQA1high classic monocytes and PTGS2high monocytes in advanced gout and overactivated CD8+ T cell subtypes in intercritical and advanced gout. Additionally, the osteoclast differentiation pathway is significantly enriched in monocytes, T cells, and B cells from advanced gout. Overall, we demonstrate systemic inflammation and distinctive immune responses in gout remission with MSU crystal deposition, allowing further exploration of the underlying mechanism and clinical significance in conversion from intercritical to advanced stage.

Evaluation of Chinese healthcare organizations' innovative performance in the digital health era.

Background: Healthcare workers' relationship with industry is not merely an agent mediating between consumer and vendor, but they are also inventors of the interventions they exist to deliver. Driven by the background of the digital health era, scientific research and technological (Sci-tech) innovation in the medical field are becoming more and more closely integrated. However, scholars shed little light on Sci-tech relevance to evaluate the innovation performance of healthcare organizations, a distinctive feature of healthcare organizations' innovation in the digital health era. Methods: Academic publications and patents are the manifestations of scientific research outputs and technological innovation outcomes, respectively. The study extracted data from publications and patents of 159 hospitals in China to evaluate their innovation performance. A total of 18 indicators were constructed, four of which were based on text similarity match and represented the Sci-tech relevance. We then applied factor analyses, analytical hierarchy process, and logistic regression to construct an evaluation model. We also examined the relationship between hospitals' innovation performance and their geographical locations. Finally, we implemented a mediation analysis to show the influence of digital health on hospital innovation performance. Results: A total of 16 indicators were involved, four of which represented the Sci-tech including the number of articles matched per patent (NAMP), the number of patents matched per article (NPMA), the proportion of highly matched patents (HMP), and the proportion of highly matched articles (HMA). Indicators of HMP (r = 0.52, P = 2.40 × 10-12), NAMP (r = 0.52, P = 2.54 × 10-12), and NPMA (r = 0.51, P = 5.53 × 10-12) showed a strong positive correlation with hospital innovation performance score. The evaluation model in this study was different from other Chinese existing hospital ranking systems. The regional innovation performance index (RIP) of healthcare organizations is highly correlated with per capita disposable income (r = 0.58) and regional GDP (r = 0.60). There was a positive correlation between digital health innovation performance scores and overall hospital innovation performance scores (r = 0.20). In addition, the hospitals' digital health innovation performance affected the hospital's overall innovation score with the mediation of Sci-tech relevance indicators (NPMA and HMA). The hospitals' digital health innovation performance score showed a significant correlation with the number of healthcare workers (r = 0.44). Conclusion: This study constructed an assessment model with four invented indicators focusing on Sci-tech relevance to provide a novel tool for researchers to evaluate the innovation performance of healthcare organizations in the digital health era. The regions with high RIP were concentrated on the eastern coastal areas with a higher level of economic development. Therefore, the promotion of scientific and technological innovation policies could be carried out in advance in areas with better economic development. The innovations in the digital health field by healthcare workers enhance the Sci-tech relevance in hospitals and boost their innovation performance. The development of digital health in hospitals depends on the input of medical personnel.

Analyzing and predicting the risk of death in stroke patients using machine learning.

Background: Stroke is an acute disorder and dysfunction of the focal neurological system that has long been recognized as one of the leading causes of death and severe disability in most regions globally. This study aimed to supplement and exploit multiple comorbidities, laboratory tests and demographic factors to more accurately predict death related to stroke, and furthermore, to make inferences about the heterogeneity of treatment in stroke patients to guide better treatment planning. Methods: We extracted data from the Medical Information Mart from the Intensive Care (MIMIC)-IV database. We compared the distribution of the demographic factors between the control and death groups. Subsequently, we also developed machine learning (ML) models to predict mortality among stroke patients. Furthermore, we used meta-learner to recognize the heterogeneity effects of warfarin and human albumin. We comprehensively evaluated and interpreted these models using Shapley Additive Explanation (SHAP) analysis. Results: We included 7,483 patients with MIMIC-IV in this study. Of these, 1,414 (18.9%) patients died during hospitalization or 30 days after discharge. We found that the distributions of age, marital status, insurance type, and BMI differed between the two groups. Our machine learning model achieved the highest level of accuracy to date in predicting mortality in stroke patients. We also observed that patients who were consistent with the model determination had significantly better survival outcomes than the inconsistent population and were better than the overall treatment group. Conclusion: We used several highly interpretive machine learning models to predict stroke prognosis with the highest accuracy to date and to identify heterogeneous treatment effects of warfarin and human albumin in stroke patients. Our interpretation of the model yielded a number of findings that are consistent with clinical knowledge and warrant further study and verification.

Alterations in smell or taste in individuals infected with SARS-CoV-2 during periods of Omicron variant dominance.

OBJECTIVES: To characterize the prevalence, severity, correlation with initial symptoms, and role of vaccination in patients with COVID-19 with smell or taste alterations (STAs). METHODS: We conducted an observational study of patients infected with SARS-CoV-2 Omicron admitted to three hospitals between May 17 and June 16, 2022. The olfactory and gustatory functions were evaluated using the taste and smell survey and the numerical visual analog scale at two time points. RESULTS: The T1 and T2 time point assessments were completed by 688 and 385 participants, respectively. The prevalence of STAs at two time points was 41.3% vs 42.6%. Furthermore, no difference existed in the severity distribution of taste and smell survey, smell, or taste visual analog scale scores between the groups. Patients with initial symptoms of headache (P = 0.03) and muscle pain (P = 0.04) were more likely to develop STAs, whereas higher education; three-dose vaccination; no symptoms yet; or initial symptoms of cough, throat discomfort, and fever demonstrated protective effects, and the results were statistically significant. CONCLUSION: The prevalence of STAs did not decrease significantly during the Omicron dominance, but the severity was reduced, and vaccination demonstrated a protective effect. In addition, the findings suggest that the presence of STAs is likely to be an important indicator of viral invasion of the nervous system.

The Influence of Childhood Trauma and Family Functioning on Internet Addiction in Adolescents: A Chain-Mediated Model Analysis.

OBJECTIVE: This study aimed to examine the prevalence of Internet addiction in adolescents, analyze the associations of childhood trauma, systematic family dynamics, and family functioning with Internet addiction, and investigate the mediating chain role of anxiety and depression in the relationship of childhood trauma and family functioning with adolescent Internet addiction. METHODS: This was a cross-sectional study in which general sociodemographic data were obtained from 3357 adolescents in grades 6-12 who were assessed using psychometric instruments such as the Childhood Trauma Questionnaire, Young Internet Addiction Test, Systematic Family Dynamics Self-Rating Scale (SSFD), Family Functioning Assessment (FAD), Self-Rating Depression Scale (SDS), and Self-Rating Anxiety Scale (SAS). RESULTS: (1) The prevalence of Internet addiction among adolescents was 26.09% (876/3357). The prevalence of childhood trauma was 54.96% (1845/3357), and the prevalence of Internet addiction was significantly different between adolescents who suffered childhood trauma and those who did not (χ2 = 96.801, ν = 1, p = 0.000). (2) Childhood trauma and various dimensions of systematic family dynamics had a significant negative and positive relationship with poor family functioning and anxiety or depression, respectively. (3) Childhood trauma was a positive predictor of Internet addiction through the chain-mediated effect of anxiety and depression, but there were no direct effects. Poor family functioning was a positive predictor of adolescent Internet addiction, and this positive prediction was augmented by the chain-mediated effect of anxiety and depression. CONCLUSIONS: Childhood trauma and poor family functioning or support predicted Internet addiction in adolescents, with anxiety and depression as mediators.

Prediction of acute kidney injury in patients with femoral neck fracture utilizing machine learning.

Background: Acute kidney injury (AKI) is a common complication associated with significant morbidity and mortality in high-energy trauma patients. Given the poor efficacy of interventions after AKI development, it is important to predict AKI before its diagnosis. Therefore, this study aimed to develop models using machine learning algorithms to predict the risk of AKI in patients with femoral neck fractures. Methods: We developed machine-learning models using the Medical Information Mart from Intensive Care (MIMIC)-IV database. AKI was predicted using 10 predictive models in three-time windows, 24, 48, and 72 h. Three optimal models were selected according to the accuracy and area under the receiver operating characteristic curve (AUROC), and the hyperparameters were adjusted using a random search algorithm. The Shapley additive explanation (SHAP) analysis was used to determine the impact and importance of each feature on the prediction. Compact models were developed using important features chosen based on their SHAP values and clinical availability. Finally, we evaluated the models using metrics such as accuracy, precision, AUROC, recall, F1 scores, and kappa values on the test set after hyperparameter tuning. Results: A total of 1,596 patients in MIMIC-IV were included in the final cohort, and 402 (25%) patients developed AKI after surgery. The light gradient boosting machine (LightGBM) model showed the best overall performance for predicting AKI before 24, 48, and 72 h. AUROCs were 0.929, 0.862, and 0.904. The SHAP value was used to interpret the prediction models. Renal function markers and perioperative blood transfusions are the most critical features for predicting AKI. In compact models, LightGBM still performs the best. AUROCs were 0.930, 0.859, and 0.901. Conclusions: In our analysis, we discovered that LightGBM had the best metrics among all algorithms used. Our study identified the LightGBM as a solid first-choice algorithm for early AKI prediction in patients after femoral neck fracture surgery.

The Beneficial Effects of Hydrogen-Rich Saline Irrigation on Chronic Rhinitis: A Randomized, Double-Blind Clinical Trial.

Purpose: Chronic rhinitis (CR) is a common chronic inflammation of the nasal mucosa. Nasal saline irrigation has been demonstrated to be an effective treatment for CR. In this study, we investigated the beneficial effects of hydrogen-rich saline irrigation as an anti-inflammatory irrigation therapy for CR and compared its effectiveness over saline irrigation. Hydrogen-rich saline (HRS) was investigated due to its antioxidant and anti-inflammatory properties. Methods: A total of 120 patients with CR were randomly divided into two groups, patients irrigated with HR (HRS group) and the control group irrigated with saline (NS group). A randomized, double-blind control study was performed. The main observation index in this study was the total score of nasal symptoms (TNSS). In addition, eosinophilic protein (ECP) of the nasal secretions, nasal nitric oxide (nNO) levels, and levels of regulatory T cells (Treg) and regulatory B cells (Breg) were also compared between the two groups. Furthermore, patients with allergic rhinitis (AR) and non-allergic rhinitis (NAR) were also evaluated based on serum-specific IgE positivity. Results: After treatment, TNSS and nasal ECP in the two groups decreased significantly (P<0.05), with patients in the HRS group showing significantly lower levels compared to the NS group (P<0.05). There were no significant differences in Treg and Breg levels between the two groups. Subgroup analysis showed that TNSS in the AR-HRS group showed a more significant reduction compared to the AR-NS group (P<0.05); however, there were no significant differences for the other inflammatory biomarkers (P>0.05). ECP levels were reduced significantly in the NAR subgroup compared to NS irrigation (P<0.05). There were no obvious adverse events observed in patients during the entire treatment period. Conclusion: Compared to saline irrigation, HRS nasal irrigation was found to improve CR clinical symptoms, especially in patients with AR. HRS could effectively be used for the clinical treatment of patients with CR.

A quantitative study of bone defects in displaced femoral neck fractures based on virtual reduction techniques.

BACKGROUND: Bone defects in femoral neck fractures are strongly associated with the prognosis after internal fixation. However, qualitative analysis of bone defects in femoral neck fractures has already been performed, quantitative studies have not been reported. In this study, we aimed to systematically analyse the morphological characteristics of bone defects in patients with femoral neck fractures using computed tomography (CT) images combined with computer image analysis techniques. METHODS: Four hundred and sixty-nine patients with femoral neck fractures from January 2014 to December 2018 at two grade A tertiary hospitals were included. Models were created in Mimics software based on CT images collected within 1 week after injury and then imported into 3-matic software for virtual reduction. The volume of the bone defect (VBD), maximum defect thickness (MDT), extent of the bone defect region (EBDR) , main defect quadrant (MDQ), collapse type and fracture classification were calculated and recorded. RESULTS: The EBDR, collapse type and MDT all had a significant positive effect on the VBD (P <0.05), with a more significant effect at higher quantiles. Age also had a significant positive effect on the VBD (P < 0.05), but its effect was more pronounced at lower quantiles. Compared to non-subcapital fractures, subcapital fractures had a positive effect on the VBD only at the 50 and 75% quantiles (P < 0.01). The female sex had a significant negative effect on the VBD compared to the male sex (P < 0.05). CONCLUSION: This study established a reliable computer image processing method for quantitative analysis of the VBD in femoral neck fractures and revealed that all patients with femoral neck fractures had bone defects, which can occur at any part of the femoral neck. The EBDR, MDT, collapse type, and patient age and sex were all important risk factors for the extent of the defect and should be taken into account in surgical planning.

Bone mesenchymal stem cells (BMSCs)-derived exosomal microRNA-21-5p regulates Kruppel-like factor 3 (KLF3) to promote osteoblast proliferation in vitro.

Bone mesenchymal stem cells (BMSCs)-derived exosomes (Exos) play important roles in osteoporosis, while the regulation of microRNA (miR)-21-5p remains unclear. The BMSCs-derived exosomes were isolated from femoral bone marrow of trauma patients, which were then used to stimulate human osteoblasts (hFOB1.19 cells). The miR-21-5p mimic or inhibitor was transfected into BMSCs to overexpress or knockdown miR-21-5p. The functions of miR-21-5p in osteoporosis were assessed by cell counting kit-8 (CCK-8) assay, alkaline phosphatase (ALP) staining and alizarin red staining assays. We found that BMSCs-derived exosomes could enhance proliferation, osteoblastic differentiation and ALP activity of hFOB1.19 cells. BMSCs-derived exosomes with upregulated miR-21-5p could further enhance these protective impacts compared with that in BMSCs-derived exosomes, while BMSCs-derived exosomes with downregulated miR-21-5p reduced these cell phenotypes. MiR-21-5p could directly bind to the 3'-untranslated region (UTR) of Kruppel-like factor 3 (KLF3), and knockdown of KLF3 obviously attenuated these inhibitory effects of BMSCs-derived exosomes with downregulated miR-21-5p on osteoblastic differentiation and ALP activity of hFOB1.19 cells. In summary, BMSCs-derived exosomal miR-21-5p improved osteoporosis through regulating KLF3, providing a potential therapeutic strategy for osteoporosis.

[Femoral head necrosis rate and risk factors after internal fixation of femoral neck fracture:a Meta-analysis].

OBJECTIVE: To study the incidence and risk factors of osteonecrosis of the femoral head (ONFH) after internal fixation in adult patients with femoral neck fracture (FNF) after 2000, and identify high-risk population of ONFH. METHODS: PubMed, Medline, The Cochrane Library, CNKI, Wanfang and VIP Database were searched to collect all the literatures on ONFH and related risk factors after internal fixation of FNF from January 1th 2000 to July 1th 2020. Study extraction was performed according to inclusion and exclusion criteria. Endnote X9 and Excel 2019 were used for literatures extraction, management and data entry, and R Studio 3.6.5 software was used for Meta-analysis. Subgroup analysis, sensitivity analysis and publication bias detection were used to explore the sources of heterogeneity and the reliability of the evaluation results. RESULTS: A total of 16 studies with 5521 patients were included. Meta-analysis showed that the incidence of ONFH after internal fixation for adult FNF was 14.5% [95% CI(0.126-0.165)]. Fracture displacement[OR=0.27, 95%CI(0.21-0.35)] and reduction quality [OR=0.15, 95%CI(0.09-0.27)] were related risk factors for ONFH. The results of subgroup rate analysis showed that the non-displaced fracture necrosis rate was 6.2%[95%CI(0.051-0.077)] and the displaced fracture necrosis rate was 20.4% [95%CI(0.166-0.249)];the good reduction fracture necrosis rate was 8.3%[95%CI(0.072-0.095)] and the poor reduction fracture necrosis rate was 35.5%[95%CI(0.233-0.500)]. The included literatures have good consistency and no publication bias. CONCLUSION: After 2000, the total incidence of ONFH after internal fixation of adult FNF has decreased, while the necrosis rates of patients with displaced fracture and poor reduction are still at a high level. The interval between injury and surgery was not analyzed in this study because of the inconstant division in the original literature.

The potency of psychiatric questionnaires to distinguish major mental disorders in Chinese outpatients.

Background: Considering the huge population in China, the available mental health resources are inadequate. Thus, our study aimed to evaluate whether mental questionnaires, serving as auxiliary diagnostic tools, have efficient diagnostic ability in outpatient psychiatric services. Methods: We conducted a retrospective study of Chinese psychiatric outpatients. Altogether 1,182, 5,069, and 4,958 records of Symptom Checklist-90 (SCL-90), Hamilton Anxiety Rating Scale (HAM-A), and Hamilton Depression Rating Scale (HAM-D), respectively, were collected from March 2021 to July 2022. The Mann-Whitney U test was applied to subscale scores and total scores of SCL-90, HAM-A, and HAM-D between the two sexes (male and female groups), different age groups, and four diagnostic groups (anxiety disorder, depressive disorder, bipolar disorder, and schizophrenia). Kendall's tau coefficient analysis and machine learning were also conducted in the diagnostic groups. Results: We found significant differences in most subscale scores for both age and gender groups. Using the Mann-Whitney U test and Kendall's tau coefficient analysis, we found that there were no statistically significant differences in diseases in total scale scores and nearly all subscale scores. The results of machine learning (ML) showed that for HAM-A, anxiety had a small degree of differentiation with an AUC of 0.56, while other diseases had an AUC close to 0.50. As for HAM-D, bipolar disorder was slightly distinguishable with an AUC of 0.60, while the AUC of other diseases was lower than 0.50. In SCL-90, all diseases had a similar AUC; among them, bipolar disorder had the lowest score, schizophrenia had the highest score, while anxiety and depression both had an AUC of approximately 0.56. Conclusion: This study is the first to conduct wide and comprehensive analyses on the use of these three scales in Chinese outpatient clinics with both traditional statistical approaches and novel machine learning methods. Our results indicated that the univariate subscale scores did not have statistical significance among our four diagnostic groups, which highlights the limit of their practical use by doctors in identifying different mental diseases in Chinese outpatient psychiatric services.

Prediction Model of Osteonecrosis of the Femoral Head After Femoral Neck Fracture: Machine Learning-Based Development and Validation Study.

BACKGROUND: The absolute number of femoral neck fractures (FNFs) is increasing; however, the prediction of traumatic femoral head necrosis remains difficult. Machine learning algorithms have the potential to be superior to traditional prediction methods for the prediction of traumatic femoral head necrosis. OBJECTIVE: The aim of this study is to use machine learning to construct a model for the analysis of risk factors and prediction of osteonecrosis of the femoral head (ONFH) in patients with FNF after internal fixation. METHODS: We retrospectively collected preoperative, intraoperative, and postoperative clinical data of patients with FNF in 4 hospitals in Shanghai and followed up the patients for more than 2.5 years. A total of 259 patients with 43 variables were included in the study. The data were randomly divided into a training set (181/259, 69.8%) and a validation set (78/259, 30.1%). External data (n=376) were obtained from a retrospective cohort study of patients with FNF in 3 other hospitals. Least absolute shrinkage and selection operator regression and the support vector machine algorithm were used for variable selection. Logistic regression, random forest, support vector machine, and eXtreme Gradient Boosting (XGBoost) were used to develop the model on the training set. The validation set was used to tune the model hyperparameters to determine the final prediction model, and the external data were used to compare and evaluate the model performance. We compared the accuracy, discrimination, and calibration of the models to identify the best machine learning algorithm for predicting ONFH. Shapley additive explanations and local interpretable model-agnostic explanations were used to determine the interpretability of the black box model. RESULTS: A total of 11 variables were selected for the models. The XGBoost model performed best on the validation set and external data. The accuracy, sensitivity, and area under the receiver operating characteristic curve of the model on the validation set were 0.987, 0.929, and 0.992, respectively. The accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve of the model on the external data were 0.907, 0.807, 0.935, and 0.933, respectively, and the log-loss was 0.279. The calibration curve demonstrated good agreement between the predicted probability and actual risk. The interpretability of the features and individual predictions were realized using the Shapley additive explanations and local interpretable model-agnostic explanations algorithms. In addition, the XGBoost model was translated into a self-made web-based risk calculator to estimate an individual's probability of ONFH. CONCLUSIONS: Machine learning performs well in predicting ONFH after internal fixation of FNF. The 6-variable XGBoost model predicted the risk of ONFH well and had good generalization ability on the external data, which can be used for the clinical prediction of ONFH after internal fixation of FNF.

Clinical Value and Pathologic Basis of Cystic Airspace Within Subsolid Nodules Confirmed as Lung Adenocarcinomas by Surgery.

PURPOSE: To investigate the clinical value and pathologic basis of cystic airspace within lung adenocarcinomas manifesting as subsolid nodules. PATIENTS AND METHODS: A retrospective study was conducted on a total of 541 surgically confirmed lung adenocarcinomas manifesting as subsolid nodules in computed tomography images, including 87 cases with cystic airspace and 454 cases without cystic airspace. The pathologic characteristics of the cases with and without cystic airspace were compared. The investigation of the pathologic structure of cystic airspace was attempted on the postoperative paraffin sections. RESULTS: There was a significant difference in the containing of cystic airspace between preinvasive and invasive adenocarcinomas (10.5 vs 26.6%; P < .001). Multivariate analysis indicated that cystic airspace is an independent predictor of invasive adenocarcinomas (odds ratio, 3.220; 95% confidence interval, 1.822-5.687). Nodules containing multiple cystic airspaces are more likely to be invasive adenocarcinomas than nodules with a single cystic airspace (47.1 vs 72.2%; P < .05). On paraffin sections, the walls of the cystic airspace seemed to be mainly composed of atypical hyperplasia and/or tumor cells on the surface and the remaining smooth muscle cells and stroma below, which is similar to the structure of bronchi. CONCLUSIONS: Cystic airspace may be a reliable predictor of invasive adenocarcinomas, the classification method based on the number of cystic airspaces might be suitable for the computed tomography-based typing of cystic airspace within subsolid nodules. Cystic airspace may derive from the destroyed and enlarged bronchi owing to the growth or infiltration of atypical hyperplasia and/or tumor cells.