The Semmes Weinstein monofilament examination as a screening tool for diabetic peripheral neuropathy

ObjectiveThe purpose of this systematic review is to evaluate current evidence in the literature on the efficacy of Semmes Weinstein monofilament examination (SWME) in diagnosing diabetic peripheral neuropathy (DPN).MethodsThe PubMed database was searched through August 2008 for articles pertaining to DPN and SWME with no language or publication date restrictions. Studies with original data comparing the diagnostic value of SWME with that of one or more other modalities for DPN in patients with diabetes mellitus were analyzed. Data were extracted by two independent investigators. Diagnostic values were calculated after classifying data by reference test, SWME methodology, and diagnostic threshold.ResultsOf the 764 studies identified, 30 articles were selected, involving 8365 patients. There was great variation in both the reference test and the methodology of SWME. However, current literature suggests that nerve conduction study (NCS) is the gold standard for diagnosing DPN. Four studies were identified which directly compared SWME with NCS and encompassed 1065 patients with, and 52 patients without diabetes mellitus. SWME had a sensitivity ranging from 57% (95% confidence interval [CI], 44% to 68%) to 93% (95% CI, 77% to 99%), specificity ranging from 75% (95% CI, 64% to 84%) to 100% (95% CI, 63% to 100%), positive predictive value (PPV) ranging from 84% (95% CI, 74% to 90%) to 100% (95% CI, 87% to 100%), and negative predictive value (NPV) ranging from 36% (95% CI, 29% to 43%) to 94% (95% CI, 91% to 96%).ConclusionsThere is great variation in the current literature regarding the diagnostic value of SWME as a result of different methodologies. To maximize the diagnostic value of SWME, a three site test involving the plantar aspects of the great toe, the third metatarsal, and the fifth metatarsals should be used. Screening is vital in identifying DPN early, enabling earlier intervention and management to reduce the risk of ulceration and lower extremity amputation

Multidimensional Uncertainty-Aware Evidential Neural Networks

Traditional deep neural networks (NNs) have significantly contributed to the state-of-the-art performance in the task of classification under various application domains. However, NNs have not considered inherent uncertainty in data associated with the class probabilities where misclassification under uncertainty may easily introduce high risk in decision making in real-world contexts (e.g., misclassification of objects in roads leads to serious accidents). Unlike Bayesian NN that indirectly infer uncertainty through weight uncertainties, evidential NNs (ENNs) have been recently proposed to explicitly model the uncertainty of class probabilities and use them for classification tasks. An ENN offers the formulation of the predictions of NNs as subjective opinions and learns the function by collecting an amount of evidence that can form the subjective opinions by a deterministic NN from data. However, the ENN is trained as a black box without explicitly considering inherent uncertainty in data with their different root causes, such as vacuity (i.e., uncertainty due to a lack of evidence) or dissonance (i.e., uncertainty due to conflicting evidence). By considering the multidimensional uncertainty, we proposed a novel uncertainty-aware evidential NN called WGAN-ENN (WENN) for solving an out-of-distribution (OOD) detection problem. We took a hybrid approach that combines Wasserstein Generative Adversarial Network (WGAN) with ENNs to jointly train a model with prior knowledge of a certain class, which has high vacuity for OOD samples. Via extensive empirical experiments based on both synthetic and real-world datasets, we demonstrated that the estimation of uncertainty by WENN can significantly help distinguish OOD samples from boundary samples. WENN outperformed in OOD detection when compared with other competitive counterparts.Comment: AAAI 202

Towards long-tailed, multi-label disease classification from chest X-ray: Overview of the CXR-LT challenge

Many real-world image recognition problems, such as diagnostic medical imaging exams, are "long-tailed" \unicode{x2013} there are a few common findings followed by many more relatively rare conditions. In chest radiography, diagnosis is both a long-tailed and multi-label problem, as patients often present with multiple findings simultaneously. While researchers have begun to study the problem of long-tailed learning in medical image recognition, few have studied the interaction of label imbalance and label co-occurrence posed by long-tailed, multi-label disease classification. To engage with the research community on this emerging topic, we conducted an open challenge, CXR-LT, on long-tailed, multi-label thorax disease classification from chest X-rays (CXRs). We publicly release a large-scale benchmark dataset of over 350,000 CXRs, each labeled with at least one of 26 clinical findings following a long-tailed distribution. We synthesize common themes of top-performing solutions, providing practical recommendations for long-tailed, multi-label medical image classification. Finally, we use these insights to propose a path forward involving vision-language foundation models for few- and zero-shot disease classification

Effect of probiotic Lactobacillus reuteri XC1 coexpressing endoglucanase and phytase on intestinal pH and morphology, carcass characteristics, meat quality, and serum biochemical indexes of broiler chickens

This study investigated the effect of transformed Lactobacillys reuteri on intestinal pH and morphology, carcass characteristics, meat quality, and serum biochemical indexes of broiler chickens. A total of 480 broilers were assigned to six treatment groups and fed a phosphorus-adequate diet, a phosphorus-deficient diet, or a phosphorus-deficient diet containing different L. reuteri recombinants. The results showed that transformed L. reuteri decreased the pH in the duodenum and jejunum of chickens at day 21, decreased drip loss and cooking loss of muscles, and improved muscle tenderness of chickens at days 21 and 42, but did not affect carcass characteristics and only slightly decreased abdominal fat. Transformed L. reuteri also significantly increased calcium, phosphorus, and glucose levels, decreased the uric acid level of serum at day 21, and significantly increased the glucose level and decreased the triglycerides of serum on day 42. L. reuteri pLEM4159-cel/phy increased the villi height in the duodenum of chickens at days 21 and 42. The transformed L. reuteri decreased the crypt depth in the duodenum and jejunum of chickens at day 21 and also decreased the crypt depth in the ileum and increased the villi height in the duodenum at day 42. L. reuteri pLEM4158 (phy) and L. reuteri pLEM4159-cel/phy improved the villi height in the ileum at day 42. Taken together, transformed L. reuteri can improve blood calcium, phosphorus, and glucose metabolism and intestinal development in broilers, but does not affect carcass characteristic

Economic and environmental impacts of China’s imported iron ore transport chain under road-to-rail policy: an empirical analysis based on the Bohai Economic Rim

The imported iron ore transport chain is a complex system concerning different transport modes, means, and routes within water areas and various regions on the land. However, the relevant literature has only tackled the carbon mitigation of a single transport mode in a specific region. Under China’s road-to-rail policy, this article investigates the total transport cost, carbon and nitrogen emissions of the imported iron ore transport chain between Brazil and the steel enterprises in the Bohai Economic Rim (BER), and established a model to select the optimal transport plan. Inspired by the policies calling for capacity structure reform of steel industry and energy structure adjustment of highway transport, the authors set up 12 scenarios and estimate the cost and emissions. The results demonstrate the positive economic and environmental impacts of the road-to-rail policy, the development of multimodal transport of railway and waterway (MTRW), and the capacity structure adjustment of the steel industry. However, the impacts of capacity structure adjustment might be marginalized. Besides, natural gas trucks are proved important to fulfilling the goal of clean highway transport. Finally, the discussion on the recent policy of liquidation mechanism reform reveals that the unit railway transport cost should be reduced moderately. Otherwise, the reduction cannot save total transport cost, but cause greater carbon and nitrogen emissions

Investigation on Characteristics of Microwave Treatment of Organic Matter in Municipal Dewatered Sludge

This study aimed to utilize a microwave technology to degrade active organic matters of the municipal dewatered sludge in a high-temperature environment. The effects of extraction agent, nanomaterial assistants, and microwave-absorbing agents and activating agents on the degradation efficiency were investigated. Dimethyl carbonate was used as the extraction agent. Nanostructured titanium oxide (TiO2) and zinc oxide (ZnO) exhibited effective assistance in the process of microwave treatment. We also developed a kind of microwave-absorbing agent, which was the sludge-based biological carbon. The sodium sulfate (Na2SO4), calcium hydroxide (Ca(OH)2), and magnesium chloride (MgCl2) were selected as activating agents to facilitate the organic matter discharging from the sludge. Through optimizing the experimental factors, it was confirmed that 0.1 wt% TiO2, 0.1 wt% ZnO, 2 wt% dimethyl carbonate, 10 wt% sludge-based biological carbon, 7.5 wt% Ca(OH)2, 0.5 wt% MgCl2, and 6 wt% Na2SO4 were the most appropriate addition amounts in the municipal dewatered sludge to make the organic matter decrease from 42.17% to 22.45%, and the moisture content reduce from 82.98% to 0.48% after the microwave treatment. By comparison, the organic matter degradation is almost zero, and the moisture content decreases to 8.69% without any additives. Moreover, the residual inert organic matter and sludge can be further solidified to lightweight construction materials by using liquid sodium silicate as the curing agent. The research provides a significant reference for the effective, fast, and low-cost treatment of the organic matter in the municipal sludge

KIF2C accelerates the development of non-small cell lung cancer and is suppressed by miR-186-3p via the AKT-GSK3β-β-catenin pathway

Abstract This study aimed to explore how kinesin family member 2C (KIF2C) influences the progression of non-small cell lung cancer (NSCLC). The levels of KIF2C and microRNA-186-3p (miR-186-3p) were examined by quantitative real-time polymerase chain reaction (qRT-PCR). Through the utilization of cell counting kit-8 (CCK-8) assay, colony formation assay, wound closure assay, and Transwell assay, NSCLC cell proliferation, migration, and invasion were identified, respectively. NSCLC cell apoptosis was assessed using the TUNEL assay and flow cytometry (FCM) assay. Luciferase reporter analysis was used to investigate the relationship between KIF2C and miR-186-3p. Western blot assays were conducted to investigate the influence of KIF2C on the AKT-GSK3β-β-catenin pathway. The results showed that KIF2C was up‐regulated in NSCLC cells, which predicted poor prognosis. KIF2C overexpression promoted the proliferation, migration, and invasion of NSCLC cells as well as inhibited NSCLC cell apoptosis. KIF2C was as a key target of miR-186-3p. High expression of KIF2C, meanwhile, increased the levels of β-catenin, p-GSK-3β and phosphorylated protein kinase B (p-AKT). KIF2C downregulation and miR-186-3p upregulation reversed these outcomes. As an oncogenic factor, KIF2C is negatively regulated by miR-186-3p and participates in the progression of NSCLC through the AKT-GSK3β-β-catenin pathway