Interactive boxing-cycling on frailty and activity limitations in frail and prefrail older adults: A randomized controlled trial.

BACKGROUND: Frailty is common among older adults, often associated with activity limitations during physical and walking tasks. The interactive boxing-cycling combination has the potential to be an innovative and efficient training method, and our hypothesis was that interactive boxing-cycling would be superior to stationary cycling in improving frailty and activity limitations in frail and prefrail older adults. OBJECTIVE: To examine the impact of interactive boxing-cycling on frailty and activity limitations in frail and prefrail older adults compared to stationary cycling. MATERIALS AND METHODS: A single-blinded randomized controlled trial. Forty-five participants who met at least one frailty phenotype criteria were randomly assigned to receive either interactive boxing-cycling (n = 23) or stationary-cycling (n = 22) for 36 sessions over 12 weeks. The interactive boxing-cycling was performed on a cycle boxer bike with an interactive boxing panel fixed in front of the bike. The primary outcomes were frailty status, including score and phenotypes. Secondary outcomes included activity limitations during physical and walking tasks. The pre- and post-intervention data of both groups were analyzed using a repeated measures two-way ANOVA. RESULTS: Both types of cycling significantly improved frailty scores (p<0.001). Interactive boxing-cycling was more effective than stationary cycling in reversing the frailty phenotype of muscle weakness (p = 0.03, odds ratio 9.19) and demonstrated greater improvements than stationary cycling in arm curl (p = 0.002, η2=0.20), functional reach (p = 0.001, η2=0.22), and grip strength (p = 0.02, η2=0.12) tests. Additionally, interactive boxing-cycling exhibited a greater effect on gait speed (p = 0.02, η2=0.13) and gait variability (p = 0.01, η2=0.14) during dual-task walking. CONCLUSION: In frail and prefrail older adults, interactive boxing-cycling effectively improves frailty but is not superior to stationary cycling. However, it is more effective at improving certain activity limitations. REGISTRATION NUMBER: TCTR20220328001.

Features of synchronous and metachronous dual primary gastric and colorectal cancer.

BACKGROUND: Studies evaluating the characteristics of dual primary gastric and colorectal cancer (CRC) (DPGCC) are limited. AIM: To analyze the clinicopathologic characteristics and prognosis of synchronous and metachronous cancers in patients with DPGCC. METHODS: From October 2010 to August 2021, patients with DPGCC were retrospectively reviewed. The patients with DPGCC were divided into two groups (synchronous and metachronous). We compared the overall survival (OS) between the groups using Kaplan-Meier survival methods. Univariate and multivariate analyses were performed using Cox's proportional hazards model to identify the independent prognostic factors for OS. RESULTS: Of the 76 patients with DPGCC, 46 and 30 had synchronous and metachronous cancers, respectively. The proportion of unresectable CRC in patients with synchronous cancers was higher than that in patients with metachronous cancers (28.3% vs 3.3%, P = 0.015). The majority of the second primary cancers had occurred within 5 years. Kaplan-Meier survival analysis showed that the patients with metachronous cancers had a better prognosis than patients with synchronous cancers (P = 0.010). The patients who had undergone gastrectomy (P < 0.001) or CRC resection (P < 0.001) had a better prognosis than those who had not. In the multivariate analysis, synchronous cancer [hazard ratio (HR) = 6.8, 95% confidence interval (95%CI): 2.0-22.7, P = 0.002)] and stage III-IV gastric cancer (GC) [HR = 10.0, 95%CI: 3.4-29.5, P < 0.001)] were risk prognostic factor for OS, while patients who underwent gastrectomy was a protective prognostic factor for OS [HR = 0.2, 95%CI: 0.1-0.6, P = 0.002]. CONCLUSION: Regular surveillance for metachronous cancer is necessary during postoperative follow-up. Surgical resection is the mainstay of therapy to improve the prognosis of DPGCC. The prognosis appears to be influenced by the stage of GC rather than the stage of CRC. Patients with synchronous cancer have a worse prognosis, and its treatment strategy is worth further exploration.

The effect of B-vitamins on the prevention and treatment of cardiovascular diseases: a systematic review and meta-analysis.

CONTEXT: Previous research evaluating the effects in B-vitamins on the prevention and treatment of cardiovascular disease (CVD) has substantial limitations and lacks recently published large prospective studies; hence, conducting an updated meta-analysis is needed. OBJECTIVE: We investigated the association between vitamin B status and human CVD development in order to provide more specific advice about vitamin B intake for those at risk of CVD. DATA SOURCES: Relevant articles were identified by JSTOR, PubMed, and ProQuest databases. DATA EXTRACTION: Key words used to identify the studies included the different combinations of B-vitamins, folate, folic acid, vitamin B6, vitamin B12, homocysteine, cardiovascular disease, stroke, coronary disease, myocardial infarction, and cerebrovascular and transient ischemic attack. The database search was supplemented by hand-searching of reference lists of selected articles. DATA ANALYSIS: Pooled estimates were calculated from the mean differences using a random-effects model. RESULTS: Supplementation with folic acid was reported to have a clinical benefit of significantly reducing carotid intima-media thickness. Higher intakes of folic acid, vitamin B6, and vitamin B12 were generally associated with a lower risk of CVD in the general population, except in those without normal renal function and those with unstable angina or past non-ST-elevation myocardial infarction. CONCLUSION: Vitamin B supplementation resulted in the greatest cardiovascular benefit in those with normal renal function and without unstable angina or non-ST-elevation myocardial infarction recently. Factors such as age, gender, and genetic polymorphisms contribute to varying effects.

Deep Learning Can Predict Bevacizumab Therapeutic Effect and Microsatellite Instability Directly from Histology in Epithelial Ovarian Cancer.

Epithelial ovarian cancer (EOC) remains a significant cause of mortality among gynecologic cancers, with the majority of cases being diagnosed at an advanced stage. Before targeted therapies were available, EOC treatment relied largely on debulking surgery and platinum-based chemotherapy. Vascular endothelial growth factors have been identified as inducing tumor angiogenesis. According to several clinical trials, anti-vascular endothelial growth factor-targeted therapy with bevacizumab was effective in all phases of EOC treatment. However, there are currently no biomarkers accessible for regular therapeutic use despite the importance of patient selection. Microsatellite instability (MSI), caused by a deficiency of the DNA mismatch repair system, is a molecular abnormality observed in EOC associated with Lynch syndrome. Recent evidence suggests that angiogenesis and MSI are interconnected. Developing predictive biomarkers, which enable the selection of patients who might benefit from bevacizumab-targeted therapy or immunotherapy, is critical for realizing personalized precision medicine. In this study, we developed 2 improved deep learning methods that eliminate the need for laborious detailed image-wise annotations by pathologists and compared them with 3 state-of-the-art methods to not only predict the efficacy of bevacizumab in patients with EOC using mismatch repair protein immunostained tissue microarrays but also predict MSI status directly from histopathologic images. In prediction of therapeutic outcomes, the 2 proposed methods achieved excellent performance by obtaining the highest mean sensitivity and specificity score using MSH2 or MSH6 markers and outperformed 3 state-of-the-art deep learning methods. Moreover, both statistical analysis results, using Cox proportional hazards model analysis and Kaplan-Meier progression-free survival analysis, confirm that the 2 proposed methods successfully differentiate patients with positive therapeutic effects and lower cancer recurrence rates from patients experiencing disease progression after treatment (P < .01). In prediction of MSI status directly from histopathology images, our proposed method also achieved a decent performance in terms of mean sensitivity and specificity score even for imbalanced data sets for both internal validation using tissue microarrays from the local hospital and external validation using whole section slides from The Cancer Genome Atlas archive.

Learning more from the inter-rater reliability of interstitial fibrosis assessment beyond just a statistic.

Interstitial fibrosis assessment by renal pathologists lacks good agreement, and we aimed to investigate its hidden properties and infer possible clinical impact. Fifty kidney biopsies were assessed by 9 renal pathologists and evaluated by intraclass correlation coefficients (ICCs) and kappa statistics. Probabilities of pathologists' assessments that would deviate far from true values were derived from quadratic regression and multilayer perceptron nonlinear regression. Likely causes of variation in interstitial fibrosis assessment were investigated. Possible misclassification rates were inferred on reported large cohorts. We found inter-rater reliabilities ranged from poor to good (ICCs 0.48 to 0.90), and pathologists' assessments had the worst agreements when the extent of interstitial fibrosis was moderate. 33.5% of pathologists' assessments were expected to deviate far from the true values. Variation in interstitial fibrosis assessment was found to be correlated with variation in interstitial inflammation assessment (r2 = 32.1%). Taking IgA nephropathy as an example, the Oxford T scores for interstitial fibrosis were expected to be misclassified in 21.9% of patients. This study demonstrated the complexity of the inter-rater reliability of interstitial fibrosis assessment, and our proposed approaches discovered previously unknown properties in pathologists' practice and inferred a possible clinical impact on patients.

Efficient Convolution Network to Assist Breast Cancer Diagnosis and Target Therapy.

Breast cancer is the leading cause of cancer-related deaths among women worldwide, and early detection and treatment has been shown to significantly reduce fatality rates from severe illness. Moreover, determination of the human epidermal growth factor receptor-2 (HER2) gene amplification by Fluorescence in situ hybridization (FISH) and Dual in situ hybridization (DISH) is critical for the selection of appropriate breast cancer patients for HER2-targeted therapy. However, visual examination of microscopy is time-consuming, subjective and poorly reproducible due to high inter-observer variability among pathologists and cytopathologists. The lack of consistency in identifying carcinoma-like nuclei has led to divergences in the calculation of sensitivity and specificity. This manuscript introduces a highly efficient deep learning method with low computing cost. The experimental results demonstrate that the proposed framework achieves high precision and recall on three essential clinical applications, including breast cancer diagnosis and human epidermal receptor factor 2 (HER2) amplification detection on FISH and DISH slides for HER2 target therapy. Furthermore, the proposed method outperforms the majority of the benchmark methods in terms of IoU by a significant margin (p<0.001) on three essential clinical applications. Importantly, run time analysis shows that the proposed method obtains excellent segmentation results with notably reduced time for Artificial intelligence (AI) training (16.93%), AI inference (17.25%) and memory usage (18.52%), making the proposed framework feasible for practical clinical usage.

Weakly supervised bilayer convolutional network in segmentation of HER2 related cells to guide HER2 targeted therapies.

Overexpression of human epidermal growth factor receptor 2 (HER2/ERBB2) is identified as a prognostic marker in metastatic breast cancer and a predictor to determine the effects of ERBB2-targeted drugs. Accurate ERBB2 testing is essential in determining the optimal treatment for metastatic breast cancer patients. Brightfield dual in situ hybridization (DISH) was recently authorized by the United States Food and Drug Administration for the assessment of ERRB2 overexpression, which however is a challenging task due to a variety of reasons. Firstly, the presence of touching clustered and overlapping cells render it difficult for segmentation of individual HER2 related cells, which must contain both ERBB2 and CEN17 signals. Secondly, the fuzzy cell boundaries make the localization of each HER2 related cell challenging. Thirdly, variation in the appearance of HER2 related cells is large. Fourthly, as manual annotations are usually made on targets with high confidence, causing sparsely labeled data with some unlabeled HER2 related cells defined as background, this will seriously confuse fully supervised AI learning and cause poor model outcomes. To deal with all issues mentioned above, we propose a two-stage weakly supervised deep learning framework for accurate and robust assessment of ERBB2 overexpression. The effectiveness and robustness of the proposed deep learning framework is evaluated on two DISH datasets acquired at two different magnifications. The experimental results demonstrate that the proposed deep learning framework achieves an accuracy of 96.78 ± 1.25, precision of 97.77 ± 3.09, recall of 84.86 ± 5.83 and Dice Index of 90.77 ± 4.1 and an accuracy of 96.43 ± 2.67, precision of 97.82 ± 3.99, recall of 87.14 ± 10.17 and Dice Index of 91.87 ± 6.51 for segmentation of ERBB2 overexpression on the two experimental datasets, respectively. Furthermore, the proposed deep learning framework outperforms 15 state-of-the-art benchmarked methods by a significant margin (P<0.05) with respect to IoU on both datasets.

Causal Effects of Plasma Haptoglobin Levels on Alzheimer's Disease: A Two-Sample Mendelian Randomization Study.

BACKGROUND: A connection between plasma levels of haptoglobin (Hp) and Alzheimer's disease (AD) has been shown in several observational studies. It is debatable, nonetheless, how the two are related causally. OBJECTIVE: To establish the causal relationship between Hp and AD using a two-sample Mendelian randomization (MR) study. METHODS: From the extensive genome-wide association studies and FinnGen dataset, summaries and statistics pertaining to AD were gathered. We investigated the possibility of a causal link between Hp and AD using a two-sample MR study. Inverse variance weighting was used as the primary analytical technique, and it was supported by the joint application of complementary analyses and fixed effects meta-analysis to combine results from various sources. RESULTS: Genetically determined Hp was causally associated with AD [odds ratio (OR), 1.05; 95% confidence interval (CI), 1.02 to 1.09; p = 8.96×10-4]; Inverse variance-weighted estimates coming from different data sources were combined in a meta-analysis with consistent findings (OR, 1.03; 95% CI, 1.01 to 1.05; p = 2.00×10-3). The outcomes of the inverse MR analysis showed that AD had no appreciable causal impact on Hp. CONCLUSION: The present MR analysis shows that higher plasma Hp leads to an increased risk of AD. Strategies for plasma Hp testing may open up new doors for the early diagnosis and prevention of AD.

Detection of ERBB2 and CEN17 signals in fluorescent in situ hybridization and dual in situ hybridization for guiding breast cancer HER2 target therapy.

The overexpression of the human epidermal growth factor receptor 2 (HER2) is a predictive biomarker in therapeutic effects for metastatic breast cancer. Accurate HER2 testing is critical for determining the most suitable treatment for patients. Fluorescent in situ hybridization (FISH) and dual in situ hybridization (DISH) have been recognized as FDA-approved methods to determine HER2 overexpression. However, analysis of HER2 overexpression is challenging. Firstly, the boundaries of cells are often unclear and blurry, with large variations in cell shapes and signals, making it challenging to identify the precise areas of HER2-related cells. Secondly, the use of sparsely labeled data, where some unlabeled HER2-related cells are classified as background, can significantly confuse fully supervised AI learning and result in unsatisfactory model outcomes. In this study, we present a weakly supervised Cascade R-CNN (W-CRCNN) model to automatically detect HER2 overexpression in HER2 DISH and FISH images acquired from clinical breast cancer samples. The experimental results demonstrate that the proposed W-CRCNN achieves excellent results in identification of HER2 amplification in three datasets, including two DISH datasets and a FISH dataset. For the FISH dataset, the proposed W-CRCNN achieves an accuracy of 0.970±0.022, precision of 0.974±0.028, recall of 0.917±0.065, F1-score of 0.943±0.042 and Jaccard Index of 0.899±0.073. For DISH datasets, the proposed W-CRCNN achieves an accuracy of 0.971±0.024, precision of 0.969±0.015, recall of 0.925±0.020, F1-score of 0.947±0.036 and Jaccard Index of 0.884±0.103 for dataset 1, and an accuracy of 0.978±0.011, precision of 0.975±0.011, recall of 0.918±0.038, F1-score of 0.946±0.030 and Jaccard Index of 0.884±0.052 for dataset 2, respectively. In comparison with the benchmark methods, the proposed W-CRCNN significantly outperforms all the benchmark approaches in identification of HER2 overexpression in FISH and DISH datasets (p<0.05). With the high degree of accuracy, precision and recall , the results show that the proposed method in DISH analysis for assessment of HER2 overexpression in breast cancer patients has significant potential to assist precision medicine.

Unsatisfactory reproducibility of interstitial inflammation scoring in allograft kidney biopsy.

Interstitial inflammation scoring is incorporated into the Banff Classification of Renal Allograft Pathology and is essential for the diagnosis of T-cell mediated rejection. However, its reproducibility, including inter-rater and intra-rater reliabilities, has not been carefully investigated. In this study, eight renal pathologists from different hospitals independently scored 45 kidney allograft biopsies with varying extents of interstitial inflammation. Inter-rater reliabilities and intra-rater reliabilities were investigated by kappa statistics and conditional agreement probabilities. Individual pathologists' scoring patterns were examined by chi-squared tests and proportions tests. The mean pairwise kappa values for inter-rater reliability were 0.27, 0.30, and 0.26 for the Banff i score, ti score, and i-IFTA, respectively. No rater pair performed consistently better or worse than others on all three scorings. After dichotomizing the scores into two groups (none/mild and moderate/severe inflammation), the averaged conditional agreements ranged from 47.1% to 50.0%. The distributions of the scores differed, but some pathologists persistently scored higher or lower than others. Given the important role of interstitial inflammation scoring in the diagnosis of T-cell mediated rejection, transplant practitioners should be aware of the possible clinical implications of the far-from-optimal reproducibility.

mTOR-dependent TFEB activation and TFEB overexpression enhance autophagy-lysosome pathway and ameliorate Alzheimer's disease-like pathology in diabetic encephalopathy.

BACKGROUND: Diabetic encephalopathy (DE) is a complication of type 2 diabetes mellitus (T2DM) that features Alzheimer's disease (AD)-like pathology, which can be degraded by the autophagy-lysosome pathway (ALP). Since transcription factor EB (TFEB) is a master regulator of ALP, TFEB-mediated ALP activation might have a therapeutic effect on DE, but this has yet to be investigated. METHODS: We established T2DM mouse models and cultured HT22 cells under high-glucose (HG) conditions to confirm the role of ALP in DE. To further investigate this, both mice and HT22 cells were treated with 3-methyladenine (3-MA). We also analyzed the content of TFEB in the nucleus and cytoplasm to evaluate its role in ALP. To confirm the effect of TFEB activation at the post-translational level in DE, we used rapamycin to inhibit the mechanistic target of rapamycin (mTOR). We transduced both mice and cells with TFEB vector to evaluate the therapeutic effect of TFEB overexpression on DE. Conversely, we conducted TFEB knockdown to verify its role in DE in another direction. RESULTS: We found that T2DM mice experienced compromised cognitive function, while HG-cultured HT22 cells exhibited increased cell apoptosis. Additionally, both T2DM mice and HG-cultured HT22 cells showed impaired ALP and heavier AD-like pathology. This pathology worsened after treatment with 3-MA. We also observed decreased TFEB nuclear translocation in both T2DM mice and HG-cultured HT22 cells. However, inhibiting mTOR with rapamycin or overexpressing TFEB increased TFEB nuclear translocation, enhancing the clearance of ALP-targeted AD-like pathology. This contributed to protection against neuronal apoptosis and alleviation of cognitive impairment. Conversely, TFEB knockdown lessened ALP-targeted AD-like pathology clearance and had a negative impact on DE. CONCLUSION: Our findings suggest that impaired ALP is responsible for the aggravation of AD-like pathology in T2DM. We propose that mTOR-dependent TFEB activation and TFEB overexpression are promising therapeutic strategies for DE, as they enhance the clearance of ALP-targeted AD-like pathology and alleviate neuronal apoptosis. Our study provides insight into the underlying mechanisms of DE and offers potential avenues for the development of new treatments for this debilitating complication of T2DM. Video abstract.

Feasibility of an Individualized mHealth Nutrition (iNutrition) Intervention for Post-Discharged Gastric Cancer Patients Following Gastrectomy: A Randomized Controlled Pilot Trial.

(1) Background: A major challenge for post-discharged gastric cancer patients following gastrectomy is the impact of the anatomy change on decreased oral intake, nutritional status, and, ultimately, quality of life. The purpose of this study is to examine the feasibility and preliminary effects of an individualized mHealth nutrition (iNutrition) intervention in post-discharged gastric cancer patients following gastrectomy. (2) Methods: A mixed-method feasibility study with a parallel randomized controlled design was conducted. Patients were randomly assigned to either the iNutrition intervention group (n = 12) or the control group (n = 12). Participants completed measures at baseline (T0), four (T1), and twelve weeks (T2) post-randomization. (3) Results: Recruitment (33%) and retention (87.5%) rates along with high adherence and acceptability supported the feasibility of the iNutrition intervention for post-discharged gastric cancer patients following gastrectomy, echoed by the qualitative findings. The iNutrition intervention significantly improved participants' nutritional behavior (p = 0.005), energy intake (p = 0.038), compliance with energy requirements (p = 0.006), and compliance with protein requirements (p = 0.008). (4) Conclusions: The iNutrition intervention is feasible and potentially benefits post-discharged gastric cancer patients following gastrectomy. A larger trial is required to establish the efficacy of this approach. Trial Registration: 19 October 2022 Chinese Clinical Trial Registry, ChiCTR2200064807.

Interpretable attention-based deep learning ensemble for personalized ovarian cancer treatment without manual annotations.

Inhibition of pathological angiogenesis has become one of the first FDA approved targeted therapies widely tested in anti-cancer treatment, i.e. VEGF-targeting monoclonal antibody bevacizumab, in combination with chemotherapy for frontline and maintenance therapy for women with newly diagnosed ovarian cancer. Identification of the best predictive biomarkers of bevacizumab response is necessary in order to select patients most likely to benefit from this therapy. Hence, this study investigates the protein expression patterns on immunohistochemical whole slide images of three angiogenesis related proteins, including Vascular endothelial growth factor, Angiopoietin 2 and Pyruvate kinase isoform M2, and develops an interpretable and annotation-free attention based deep learning ensemble framework to predict the bevacizumab therapeutic effect on patients with epithelial ovarian cancer or peritoneal serous papillary carcinoma using tissue microarrays (TMAs). In evaluation with five-fold cross validation, the proposed ensemble model using the protein expressions of both Pyruvate kinase isoform M2 and Angiopoietin 2 achieves a notably high F-score (0.99±0.02), accuracy (0.99±0.03), precision (0.99±0.02), recall (0.99±0.02) and AUC (1.00±0). Kaplan-Meier progression free survival analysis confirms that the proposed ensemble is able to identify patients in the predictive therapeutic sensitive group with low cancer recurrence (p<0.001), and the Cox proportional hazards model analysis further confirms the above statement (p=0.012). In conclusion, the experimental results demonstrate that the proposed ensemble model using the protein expressions of both Pyruvate kinase isoform M2 and Angiopoietin 2 can assist treatment planning of bevacizumab targeted therapy for patients with ovarian cancer.

Using Arterial Pulse and Laser Doppler Analyses to Discriminate between the Cardiovascular Effects of Different Running Levels.

BACKGROUND AND AIMS: Running can induce advantageous cardiovascular effects such as improved arterial stiffness and blood-supply perfusion. However, the differences between the vascular and blood-flow perfusion conditions under different levels of endurance-running performance remains unclear. The present study aimed to assess the vascular and blood-flow perfusion conditions among 3 groups (44 male volunteers) according to the time taken to run 3 km: Level 1, Level 2, and Level 3. METHODS: The radial blood pressure waveform (BPW), finger photoplethygraphy (PPG), and skin-surface laser-Doppler flowmetry (LDF) signals of the subjects were measured. Frequency-domain analysis was applied to BPW and PPG signals; time- and frequency-domain analyses were applied to LDF signals. RESULTS: Pulse waveform and LDF indices differed significantly among the three groups. These could be used to evaluate the advantageous cardiovascular effects provided by long-term endurance-running training, such as vessel relaxation (pulse waveform indices), improvement in blood supply perfusion (LDF indices), and changes in cardiovascular regulation activities (pulse and LDF variability indices). Using the relative changes in pulse-effect indices, we achieved almost perfect discrimination between Level 3 and Level 2 (AUC = 0.878). Furthermore, the present pulse waveform analysis could also be used to discriminate between the Level-1 and Level-2 groups. CONCLUSIONS: The present findings contribute to the development of a noninvasive, easy-to-use, and objective evaluation technique for the cardiovascular benefits of prolonged endurance-running training.

Advanced lung cancer inflammation index predicts the outcomes of patients with non-metastatic gastric cancer after radical surgical resection.

Background: The prognostic value of the advanced lung cancer inflammation index (ALI) has been demonstrated in various tumors. However, the prognostic significance of ALI in non-metastatic gastric cancer (GC) remains unclear. This study aimed to identify the prognostic values of ALI in patients with non-metastatic GC who underwent radical surgical resection. Methods: Patients who underwent radical surgery for non-metastatic GC from January 2008 to September 2020 were enrolled in this study. The preoperative ALI was calculated as follows: body mass index × serum albumin/neutrophil to lymphocyte ratio. The primary outcomes were overall survival (OS) and cancer-specific survival (CSS). Cox regression analyses were performed to assess the association between ALI and survival. The potential of ALI was supported by sensitivity testing based on the propensity score matching (PSM) analysis. Results: Low preoperative ALI was significantly correlated with male gender (P=0.037), older age (P=0.004), T3/4 stage (P=0.001), lymph node metastasis (P=0.030), Tumor Node Metastasis (TNM) stage classification progression (P=0.004), and vessel invasion (P=0.001). Patients with low ALI showed worse OS (P<0.001) and CSS (P=0.001) compared to those with high ALI. Multivariable analysis showed that ALI was an independent prognostic factor for both OS [hazard ratio (HR) =1.55; 95% confidence interval (CI), 1.11-2.16]; P=0.010] and CSS (HR =1.46; 95% CI, 1.01-2.10; P=0.043) in non-metastatic GC patients who underwent radical surgical resection. Further PSM analysis confirmed the prognostic value of ALI in the PSM cohort. Conclusions: The preoperative ALI is associated with survival outcomes in patients who have undergone radical surgical resection for non-metastatic GC. Low ALI appears to predict a worse prognosis.

Increased hip adductor activation during sit-to-stand improves muscle activation timing and rising-up mechanics in individuals with hemiparesis.

Long sit-to-stand (STS) time has been identified as a feature of impaired functional mobility. The changes in biomechanics of STS performance with simultaneous hip adductor contraction have not been studied, which may limit indications for use of hip adductor activation during STS training. Ten individuals with hemiplegia (mean age 61.8 years, injury time 29.8 ± 15.2 months) performed the STS with and without squeezing a ball between two legs. The joint moments, ground reaction force (GRF), chair reaction force and movement durations and temporal index of electromyography were calculated from the control condition for comparison with those from the ball squeezing condition. Under the squeeze condition, reduced peak vertical GRF during the ascension phase with increased loading rate was observed in the nonparetic limb, and the peak knee extensor moment occurred earlier in the paretic. Earlier activation of tibialis anterior and gluteus maximus, and gluteus medius were found in squeeze STS. Squeezing a ball between limbs during STS increased the contraction timing of tibialis anterior, gluteus maximus, gluteus medius, and soleus as well as a more symmetric rising mechanics encourage the use of squeezing a ball between limbs during STS for individuals with hemiparesis.

Annotation-Free Deep Learning-Based Prediction of Thyroid Molecular Cancer Biomarker BRAF (V600E) from Cytological Slides.

Thyroid cancer is the most common endocrine cancer. Papillary thyroid cancer (PTC) is the most prevalent form of malignancy among all thyroid cancers arising from follicular cells. Fine needle aspiration cytology (FNAC) is a non-invasive method regarded as the most cost-effective and accurate diagnostic method of choice in diagnosing PTC. Identification of BRAF (V600E) mutation in thyroid neoplasia may be beneficial because it is specific for malignancy, implies a worse prognosis, and is the target for selective BRAF inhibitors. To the authors' best knowledge, this is the first automated precision oncology framework effectively predict BRAF (V600E) immunostaining result in thyroidectomy specimen directly from Papanicolaou-stained thyroid fine-needle aspiration cytology and ThinPrep cytological slides, which is helpful for novel targeted therapies and prognosis prediction. The proposed deep learning (DL) framework is evaluated on a dataset of 118 whole slide images. The results show that the proposed DL-based technique achieves an accuracy of 87%, a precision of 94%, a sensitivity of 91%, a specificity of 71% and a mean of sensitivity and specificity at 81% and outperformed three state-of-the-art deep learning approaches. This study demonstrates the feasibility of DL-based prediction of critical molecular features in cytological slides, which not only aid in accurate diagnosis but also provide useful information in guiding clinical decision-making in patients with thyroid cancer. With the accumulation of data and the continuous advancement of technology, the performance of DL systems is expected to be improved in the near future. Therefore, we expect that DL can provide a cost-effective and time-effective alternative tool for patients in the era of precision oncology.

Rapid Detection of Fusarium oxysporum Using Insulated Isothermal PCR and a Rapid, Simple DNA Preparation Protocol.

We developed an insulated isothermal PCR (iiPCR) method for the efficient and rapid detection of Fusarium oxysporum (Fo), which is a fungus that infects various hosts and causes severe crop losses. The Fo iiPCR method was sensitive enough to detect up to 100 copies of standard DNA template and 10 fg of Fo genomic DNA. In addition, it could directly detect 1 pg of mycelium and 10 spores of Fo without DNA extraction. Our study compared the performance of Fo iiPCR to that of three published in planta molecular detection methods-conventional PCR, SYBR green-based real-time PCR, and hydrolysis probe-based real-time PCR-in field detection of Fo. All diseased field samples yielded positive detection results with high reproducibility when subjected to an Fo iiPCR test combined with a rapid DNA extraction protocol compared to Fo iiPCR with an automated magnetic bead-based DNA extraction protocol. Intraday and interday assays were performed to ensure the stability of this new rapid detection method. The results of detection of Fo in diseased banana pseudostem samples demonstrated that this new rapid detection method was suitable for field diagnosis of Fusarium wilt and had high F1 scores for detection (the harmonic mean of precision and recall of detection) for all asymptomatic and symptomatic Fo-infected banana samples. In addition, banana samples at four growth stages (seedling, vegetative, flowering and fruiting, and harvesting) with mild symptoms also showed positive detection results. These results indicate that this new rapid detection method is a potentially efficient procedure for on-site detection of Fo.

A Soft Label Deep Learning to Assist Breast Cancer Target Therapy and Thyroid Cancer Diagnosis.

According to the World Health Organization Report 2022, cancer is the most common cause of death contributing to nearly one out of six deaths worldwide. Early cancer diagnosis and prognosis have become essential in reducing the mortality rate. On the other hand, cancer detection is a challenging task in cancer pathology. Trained pathologists can detect cancer, but their decisions are subjective to high intra- and inter-observer variability, which can lead to poor patient care owing to false-positive and false-negative results. In this study, we present a soft label fully convolutional network (SL-FCN) to assist in breast cancer target therapy and thyroid cancer diagnosis, using four datasets. To aid in breast cancer target therapy, the proposed method automatically segments human epidermal growth factor receptor 2 (HER2) amplification in fluorescence in situ hybridization (FISH) and dual in situ hybridization (DISH) images. To help in thyroid cancer diagnosis, the proposed method automatically segments papillary thyroid carcinoma (PTC) on Papanicolaou-stained fine needle aspiration and thin prep whole slide images (WSIs). In the evaluation of segmentation of HER2 amplification in FISH and DISH images, we compare the proposed method with thirteen deep learning approaches, including U-Net, U-Net with InceptionV5, Ensemble of U-Net with Inception-v4, Inception-Resnet-v2 encoder, and ResNet-34 encoder, SegNet, FCN, modified FCN, YOLOv5, CPN, SOLOv2, BCNet, and DeepLabv3+ with three different backbones, including MobileNet, ResNet, and Xception, on three clinical datasets, including two DISH datasets on two different magnification levels and a FISH dataset. The result on DISH breast dataset 1 shows that the proposed method achieves high accuracy of 87.77 ± 14.97%, recall of 91.20 ± 7.72%, and F1-score of 81.67 ± 17.76%, while, on DISH breast dataset 2, the proposed method achieves high accuracy of 94.64 ± 2.23%, recall of 83.78 ± 6.42%, and F1-score of 85.14 ± 6.61% and, on the FISH breast dataset, the proposed method achieves high accuracy of 93.54 ± 5.24%, recall of 83.52 ± 13.15%, and F1-score of 86.98 ± 9.85%, respectively. Furthermore, the proposed method outperforms most of the benchmark approaches by a significant margin (p <0.001). In evaluation of segmentation of PTC on Papanicolaou-stained WSIs, the proposed method is compared with three deep learning methods, including Modified FCN, U-Net, and SegNet. The experimental result demonstrates that the proposed method achieves high accuracy of 99.99 ± 0.01%, precision of 92.02 ± 16.6%, recall of 90.90 ± 14.25%, and F1-score of 89.82 ± 14.92% and significantly outperforms the baseline methods, including U-Net and FCN (p <0.001). With the high degree of accuracy, precision, and recall, the results show that the proposed method could be used in assisting breast cancer target therapy and thyroid cancer diagnosis with faster evaluation and minimizing human judgment errors.