Efficacy and safety of anti-programmed death-1 antibody-based combination therapy in advanced or metastatic gastric or gastroesophageal junction cancer in Chinese patients: A real-world study.

PURPOSE: Programmed death-1 antibody plus chemotherapy has gained approval for the treatment for (human epidermal growth factor receptor 2 negative locally advanced or metastatic gastric or gastroesophageal junction cancer. This study aims to analyze the efficacy and safety of anti-programmed death-1 antibody combined with chemo- or anti-angiogenesis therapy in Chinese patients with advanced or metastatic gastric or gastroesophageal junction cancer in a real-world setting. METHODS: In total, 122 patients treated with anti-programmed death-1 antibody-based combination therapy between April 2019 and December 2021 were encompassed. Clinical outcomes and safety profile were measured and analyzed. RESULTS: In the whole cohort, median overall survival was 17.2 months, median progression-free survival was 10.9 months, and median duration of response was 9.4 months. Notably, in the first-line patients, the median overall survival was not reached, median progression-free survival was 14.8 months, objective response rate was 68.4%. In the second-line group, median overall survival, median progression-free survival, median duration of response, and objective response rate were 10.9 months, 5.9 months, 4.5 months, and 41.5%, respectively. Treatment-related adverse events of any grade were observed in 28.2% of the overall cohort, primarily affecting the hematological and liver function. Grade 3 or 4 adverse events were mainly characterized by increased levels of aspartate aminotransferase, alanine aminotransferase, along with decreased lymphocyte and white blood cells, as well as anemia. CONCLUSIONS: Patients in our cohort experienced a clinical benefit from anti-programmed death-1 antibody-combined treatment in first-line treatment settings, with acceptable treatment-related adverse events. The benefit of anti-programmed death-1 antibody combined with chemo- or anti-angiogenesis treatment to the second-line patients should be further confirmed by large multi-center randomized, controlled clinical trials.

How Does Masculinity Play a Protective Role for Gay Men? The Mediation Effect of Mindfulness Between Gender Role and Mental Distress.

Masculinity is validated as a protective factor in mental health for gay population. However, potential mediators between masculinity and mental health remain unclear. Mindfulness, as one of the individual's traits has been proved to play an essential role on mental health. Yet the correlation between mindfulness and masculinity has barely been examined, and whether mindfulness could serve as a key mediator to explain the protective effect masculinity bringing to mental health for gay men remains unknown. To test this hypothesis, we recruited 210 gay men in China to conduct online questionnaires containing scales of FFMQ, BSRI, DASS-21 and demographic features. Based on mediation analysis, we found among gay men, mindfulness significantly mediates the negative relationship between masculinity and stress (SIE (standardized indirect effect) = -.20, 95% CI [-.28 -.11]), anxiety (SIE = -.17, 95% CI [-.26 -.09]) and depression (SIE = -.20, 95% CI [-.29 -.11]). Furthermore, by decomposing sub-dimensions of mindfulness, we found both "describing" and "acting with awareness" exhibit significant mediation effects between masculinity and mental distress. We further found "being analytical", one key sub-dimension of masculinity, positively correlates with mindful describing (r = .369, p < .001). Our results indicate that trait mindfulness serves as a core mediator between masculinity and mental health, the key trait in masculinity (being analytical) closely connects with the essential element of mindfulness (describing) and low in masculinity might undermine gay men's abilities of acting with awareness (staying focused). Our findings may also shed light on developing gay men-aimed mindfulness-based clinical interventions.

A CNN model for early detection of pepper Phytophthora blight using multispectral imaging, integrating spectral and textural information.

BACKGROUND: Pepper Phytophthora blight is a devastating disease during the growth process of peppers, significantly affecting their yield and quality. Accurate, rapid, and non-destructive early detection of pepper Phytophthora blight is of great importance for pepper production management. This study investigated the possibility of using multispectral imaging combined with machine learning to detect Phytophthora blight in peppers. Peppers were divided into two groups: one group was inoculated with Phytophthora blight, and the other was left untreated as a control. Multispectral images were collected at 0-h samples before inoculation and at 48, 60, 72, and 84 h after inoculation. The supporting software of the multispectral imaging system was used to extract spectral features from 19 wavelengths, and textural features were extracted using a gray-level co-occurrence matrix (GLCM) and a local binary pattern (LBP). The principal component analysis (PCA), successive projection algorithm (SPA), and genetic algorithm (GA) were used for feature selection from the extracted spectral and textural features. Two classification models were established based on effective single spectral features and significant spectral textural fusion features: a partial least squares discriminant analysis (PLS_DA) and one-dimensional convolutional neural network (1D-CNN). A two-dimensional convolutional neural network (2D-CNN) was constructed based on five principal component (PC) coefficients extracted from the spectral data using PCA, weighted, and summed with 19-channel multispectral images to create new PC images. RESULTS: The results indicated that the models using PCA for feature selection exhibit relatively stable classification performance. The accuracy of PLS-DA and 1D-CNN based on single spectral features is 82.6% and 83.3%, respectively, at the 48h mark. In contrast, the accuracy of PLS-DA and 1D-CNN based on spectral texture fusion reached 85.9% and 91.3%, respectively, at the same 48h mark. The accuracy of the 2D-CNN based on 5 PC images is 82%. CONCLUSIONS: The research indicates that Phytophthora blight infection can be detected 48 h after inoculation (36 h before visible symptoms). This study provides an effective method for the early detection of Phytophthora blight in peppers.

Development and Validation of Nomograms for Predicting Pneumonia in Patients with COVID-19 and Lung Cancer.

Background: COVID-19 has spread worldwide, becoming a global threat to public health and can lead to complications, especially pneumonia, which can be life-threatening. However, in lung cancer patients, the prediction of pneumonia and severe pneumonia has not been studied. We aimed to develop effective models to assess pneumonia after SARS-CoV-2 infection in lung cancer patients to guide COVID-19 management. Methods: We retrospectively recruited 621 lung cancer patients diagnosed with COVID-19 via SARS-CoV-2 RT-PCR analysis in two medical centers and divided into training and validation group, respectively. Univariate and multivariate logistic regression analysis were used to identify independent risk factors of all-grade pneumonia and ≥ grade 2 pneumonia in the training group. Nomograms were established based on independent predictors and verified in the validation group. C-index, ROC curves, calibration curve, and DCA were used to evaluate the nomograms. Subgroup analyses in immunotherapy or thoracic radiotherapy patients were then conducted. Results: Among 621 lung cancer patients infected with SARS-CoV-2, 203 (32.7%) developed pneumonia, and 66 (10.6%) were ≥ grade 2. Multivariate logistic regression analysis showed that diabetes, thoracic radiotherapy, low platelet and low albumin at diagnosis of COVID-19 were significantly associated with all-grade pneumonia. The C-indices of the prediction nomograms in the training group and validation group were 0.702 and 0.673, respectively. Independent predictors of ≥ grade 2 pneumonia were age, KPS, thoracic radiotherapy, platelet and albumin at COVID 19 diagnosis, with C-indices of 0.811 and 0.799 in the training and validation groups. In the thoracic radiotherapy subgroup, 40.8% and 11% patients developed all-grade and ≥grade 2 pneumonia, respectively. The rates in the immunotherapy subgroup were 31.3% and 6.6%, respectively. Conclusion: We developed nomograms predicting the probability of pneumonia in lung cancer patients infected with SARS-CoV-2. The models showed good performance and can be used in the clinical management of COVID-19 in lung cancer patients. Higher-risk patients should be managed with enhanced protective measures and appropriate intervention.

Effect of longitudinal changes of cachexia on the efficacy and toxicity of immune checkpoint inhibitors in esophageal squamous cell cancer (ESCC) patients.

PURPOSE: Immune checkpoint inhibitors (ICIs) have enhanced survival in advanced esophageal squamous cell cancer (ESCC) patients, but their efficacy varies. Cachexia, characterized by muscle loss and significant weight loss, might influence ICI response. This study examines the relationship between cachexia's longitudinal changes and ICI outcomes in ESCC patients. METHODS: ESCC patients undergoing at least two ICI cycles from 2017 to 2021 were studied. Cachexia's baseline and evolving patterns during ICI treatment were observed. Kaplan-Meier and Cox regression analyses were used to assess cachexia's effect on ICI efficacy. Chi-square tests were used to determine cachexia's link to immune-related adverse effects (irAEs). RESULTS: Two hundred seventy-eight ICI-treated patients had a median progression-free survival (PFS) of 5.78 months and overall survival (OS) of 8.3 months. Pretreatment cachexia led to worse outcomes: PFS 7.87 versus 5.3 months, time to progression (TTP) 10.9 versus 6.1 months, and OS 14.3 versus 9.2 months. Irreversible cachexia showed the poorest results. Cachexia's changes weren't associated with irAEs. CONCLUSION: Baseline and evolving cachexia significantly impact ICI efficacy in ESCC patients. Continuous cachexia monitoring during ICI therapy is crucial for optimal ESCC management.

[Meta-analysis of autologous bone grafts and bone substitute for the treatment of tibial plateau fractures].

OBJECTIVE: To explore clinical efficacy of autologous bone grafts and bone substitute for the treatment of tibial plateau fractures by Meta analysis. METHODS: Controlled clinical studies on autogenous bone transplantation and bone substitutes in treating tibial plateau fractures published on PubMed,Web of Science,CNKI,Wanfang and other databases from January 2005 to August 2022 were searched by computer. Literature screening and data extraction were performed according to randomized controlled trial(RCT),and the quality of RCT were evaluated by using intervention meta-analysis criteria in Cochrane manual. Meta-analysis of joint depression,secondary collapse rate of articular surface,blood loss,operative time and infection rate between two methods were performed by Rev Man 5.3 software. RESULTS: Seven RCT studies (424 patients) were included,296 patients in bone replacement group and 128 patients in autograft group. Operative time [MD=-16.79,95%CI(-25.72,-7.85),P=0.000 2] and blood loss[MD=-70.49,95%CI(-79.34,-61.65),P<0.000 01] between two groups had statistically differences,while joint depression[MD=-0.17,95%CI(-0.91,0.58),P=0.66],secondary collapse rate of joint surface[RR=-0.74, 95%CI(0.35,1.57),P=0.43],infection rate [RR=1.21,95%CI(0.31,4.70),P=0.78] between two groups had no differences. CONCLUSION: The effects of bone substitute and autograft for the treatment of tibial plateau fracture have similar effects in terms of joint depression,secondary articular surface collapse rate and infection rate. However,compared with autologous bone transplantation,bone replacement could reduce blood loss and shorten operation time.

From pixels to patient care: deep learning-enabled pathomics signature offers precise outcome predictions for immunotherapy in esophageal squamous cell cancer.

BACKGROUND: Immunotherapy has significantly improved survival of esophageal squamous cell cancer (ESCC) patients, however the clinical benefit was limited to only a small portion of patients. This study aimed to perform a deep learning signature based on H&E-stained pathological specimens to accurately predict the clinical benefit of PD-1 inhibitors in ESCC patients. METHODS: ESCC patients receiving PD-1 inhibitors from Shandong Cancer Hospital were included. WSI images of H&E-stained histological specimens of included patients were collected, and randomly divided into training (70%) and validation (30%) sets. The labels of images were defined by the progression-free survival (PFS) with the interval of 4 months. The pretrained ViT model was used for patch-level model training, and all patches were projected into probabilities after linear classifier. Then the most predictive patches were passed to RNN for final patient-level prediction to construct ESCC-pathomics signature (ESCC-PS). Accuracy rate and survival analysis were performed to evaluate the performance of ViT-RNN survival model in validation cohort. RESULTS: 163 ESCC patients receiving PD-1 inhibitors were included for model training. There were 486,188 patches of 1024*1024 pixels from 324 WSI images of H&E-stained histological specimens after image pre-processing. There were 120 patients with 227 images in training cohort and 43 patients with 97 images in validation cohort, with balanced baseline characteristics between two groups. The ESCC-PS achieved an accuracy of 84.5% in the validation cohort, and could distinguish patients into three risk groups with the median PFS of 2.6, 4.5 and 12.9 months (P < 0.001). The multivariate cox analysis revealed ESCC-PS could act as an independent predictor of survival from PD-1 inhibitors (P < 0.001). A combined signature incorporating ESCC-PS and expression of PD-L1 shows significantly improved accuracy in outcome prediction of PD-1 inhibitors compared to ESCC-PS and PD-L1 anlone, with the area under curve value of 0.904, 0.924, 0.610 for 6-month PFS and C-index of 0.814, 0.806, 0.601, respectively. CONCLUSIONS: The outcome supervised pathomics signature based on deep learning has the potential to enable superior prognostic stratification of ESCC patients receiving PD-1 inhibitors, which convert the images pixels to an effective and labour-saving tool to optimize clinical management of ESCC patients.

TSP-1 increases autophagy level in cartilage by upregulating HSP27 which delays progression of osteoarthritis.

This study aimed to determine whether Thrombospondin-1 (TSP-1) can be used as a biomarker to diagnose early osteoarthritis (OA) and whether it has a chondroprotective effect against OA. We examined TSP-1 expression in cartilage, synovial fluid, and serum at different time points after anterior cruciate ligament transection (ACLT) surgery in rats. Subsequently, TSP-1 was overexpressed or silenced to detect its effects on extracellular matrix (ECM) homeostasis, autophagy level, proliferation and apoptosis in chondrocytes. Adenovirus encoding TSP-1 was injected into the knee joints of ACLT rats to test its effect against OA. Combined with proteomic analysis, the molecular mechanism of TSP-1 in cartilage degeneration was explored. Intra-articular injection of an adenovirus carrying the TSP-1 sequence showed chondroprotective effects against OA. Moreover, TSP-1 expression decreases with OA progression and can effectively promote cartilage proliferation, inhibit apoptosis, and helps to sustain the balance between ECM anabolism and catabolism. Overexpression of TSP-1 also can increase autophagy by upregulating Heat Shock Protein 27 (HSP27, hspb1), thereby enhancing its effect as a stimulator of autophagy. TSP-1 is a hopeful strategy for OA treatment.