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Acute pancreatitis (AP) is a common gastrointestinal disease with high morbidity and mortality rate. Unfortunately, neither the etiology nor the pathophysiology of AP are fully understood and causal treatment options are not available. Recently we demonstrated that heparanase (Hpa) is adversely involved in the pathogenesis of AP and inhibition of this enzyme ameliorates the manifestation of the disease. Moreover, a pioneer study demonstrated that Aspirin has partial inhibitory effect on Hpa. Another compound, which possesses a mild pancreato‐protective effect against AP, is Trehalose, a common disaccharide. We hypothesized that combination of Aspirin, Trehalose, PG545 (Pixatimod) and SST0001 (Roneparstat), specific inhibitors of Hpa, may exert pancreato‐protective effect better than each drug alone. Thus, the current study examines the pancreato‐protective effects of Aspirin, Trehalose, PG545 and SST0001 in experimental model of AP induced by cerulein in wild‐type (WT) and Hpa over‐expressing (Hpa‐Tg) mice. Cerulein‐induced AP in WT mice was associated with significant rises in the serum levels of lipase (X4) and amylase (X3) with enhancement of pancreatic edema index, inflammatory response, and autophagy. Responses to cerulein were all more profound in Hpa‐Tg mice versus WT mice, evident by X7 and X5 folds increase in lipase and amylase levels, respectively. Treatment with Aspirin or Trehalose alone and even more so in combination with PG545 or SST0001 were highly effective, restoring the serum level of lipase back to the basal level. Importantly, a novel newly synthesized compound termed Aspirlose effectively ameliorated the pathogenesis of AP as a single agent. Collectively, the results strongly indicate that targeting Hpa by using anti‐Hpa drug combinations constitute a novel therapy for this common orphan disease. [ABSTRACT FROM AUTHOR]

Excess nutrient pollution contributes to the formation of harmful algal blooms (HABs) that compromise fisheries and recreation and that can directly endanger human and animal health via cyanotoxins. Efforts to quantify the occurrence, drivers, and severity of HABs across large areas is difficult due to the resource intensive nature of field monitoring of lake nutrient and chlorophyll‐a concentrations. To better characterize how nutrients interact with other environmental factors to produce algal blooms in freshwater systems, we used spatially explicit and temporally matched climate, landscape, in‐lake characteristic, and nutrient inventory data sets to predict nutrients and chlorophyll‐a across the conterminous US (CONUS). Using a nested modeling approach, three random forest (RF) models were trained to explain the spatiotemporal variation in total nitrogen (TN), total phosphorus (TP), and chlorophyll‐a concentrations across US EPA's National Lakes Assessment (n = 2,062). Concentrations of TN and TP were the most important predictors and, with other variables, the RF model accounted for 68% of variation in chlorophyll‐a. We then used these RF models to extrapolate lake TN and TP predictions to lakes without nutrient observations and predict chlorophyll‐a for ∼112,000 lakes across the CONUS. Risk for high chlorophyll‐a concentrations is highest in the agriculturally dominated Midwest, but other areas of risk emerge in nutrient pollution hot spots across the country. These catchment and lake‐specific results can help managers identify potential nutrient pollution and chlorophyll‐a hot spots that may fuel blooms, prioritize at‐risk lakes for additional monitoring, and optimize management to protect human health and other environmental end goals. Plain Language Summary: When lakes receive large amounts of nutrients from the surrounding landscape due to fertilizer runoff or other sources of nutrient pollution, they can develop algal blooms. Algal blooms are harmful to the lake ecosystem and sometimes produce toxins which are dangerous to humans and animals. To assess this issue, lake chlorophyll‐a, a measure of algal presence, is monitored. This monitoring is limited in reach due to the expense of in‐lake sampling and the limited resolution of satellite technology. However, there is a wealth of climate, nutrient, landscape, and in‐lake characteristic data for the conterminous US (CONUS) which explains much of what contributes to nutrient pollution and algal growth. Here, we use this data in a machine learning model to predict nutrient (total nitrogen and total phosphorus) and chlorophyll‐a concentrations in about 112,000 lakes in the CONUS. We found that high chlorophyll‐a concentrations are more likely in the Midwest where agriculture is prevalent, but other areas with high lake chlorophyll‐a concentrations are present across the CONUS in nutrient pollution hot spots. These predictions of lake nutrient and chlorophyll‐a concentrations can help managers identify areas of concern, prioritize at‐risk lakes for testing, and target management to protect human health and the environment. Key Points: Chlorophyll‐a and nutrient concentrations in ∼112,000 lakes were predicted using widely available national data sets and machine learningNutrients far outweigh other environmental predictors in driving chlorophyll‐a concentrations in lakesWith more information about likely chlorophyll‐a concentrations, managers can prioritize lakes at risk for harmful algal bloom production [ABSTRACT FROM AUTHOR]

A novel force sensor exploiting the interaction between plasmonic nanostructures and upconversion nanoparticles (UCNPs) is reported. The nanosensor is composed of a gold nanodisk and UCNPs separated by a flexible polymer layer. The gold nanodisk is designed to exhibit a plasmon resonance that selectively enhances one of the emission bands of the UCNPs while leaving the other ones largely unaffected. As the nanosensor is compressed or stretched by an external force, the polymer layer thickness changes, modulating the plasmon‐UCNP coupling. The resulting changes in the luminescence intensity provide the basis for sensing. Furthermore, the nanosensor employs ratiometric sensing, which makes it highly robust against any environmental variations. The nanosensors exhibit two orders of magnitude higher responsivity than previously reported UCNP‐based force sensors. They can be prepared as an on‐chip sensor array or in a colloidal solution, making them suitable for a variety of applications in biology and robotics. [ABSTRACT FROM AUTHOR]

Colorectal cancer is the third most common type of cancer. It develops slowly as a polyp that can turn into a cancerous tumor. This study aimed to develop a decision‐making algorithm of microscopic images using texture analysis that is orientation free, to be used for automated classification of normal and neoplastic (malignant or premalignant) colonic biopsies. Forty‐nine colonic adenocarcinomas, 41 adenomas and a control group of adjacent normal colonic mucosa were included in the texture analysis. Radon transform followed by the Fast Fourier transform were applied to the images. Subsequently, the gray level co‐occurrence matrix (GLCM) transform was applied allowing the extraction of four textural variables (homogeneity, contrast, correlation, and entropy). For classification and prediction of the diagnosis, a statistical multivariate regression model and a neural network (NNET) model were used and compared. The statistical model provided a sensitivity of 71.3% and a specificity of 50% (Area under the ROC curve: 0.67) for classifying the neoplastic and the normal images, respectively. The NNET model was superior to the statistical model and produced a sensitivity of 97.9% and specificity of 88% (Area under the ROC curve: 0.92). To our knowledge, this is the first study that used a combination of Radon, FFT, and GLCM transformations in order to overcome the tissue orientation problem in texture analysis of microscopic images of colonic biopsies. The NNET classifier trained by the extracted textural features proved to be superior to the statistical classifier, thus predicting colonic neoplasia with high accuracy. Research Highlights: We propose a novel decision‐making algorithm of orientation invariant image texture analysis, fast and easily implemented for automated differentiation between benign and neoplastic epithelial tumors of the colon. This method can reduce the turnaround time allowing to prioritize the biopsies during their examination and diagnosis by the pathologist. [ABSTRACT FROM AUTHOR]

The de novo design of miniprotein inhibitors has recently emerged as a new technology to create proteins that bind with high affinity to specific therapeutic targets. Their size, ease of expression, and apparent high stability makes them excellent candidates for a new class of protein drugs. However, beyond circular dichroism melts and hydrogen/deuterium exchange experiments, little is known about their dynamics, especially at the elevated temperatures they seemingly tolerate quite well. To address that and gain insight for future designs, we have focused on identifying unintended and previously overlooked heat‐induced structural and chemical changes in a particularly stable model miniprotein, EHEE_rd2_0005. Nuclear magnetic resonance (NMR) studies suggest the presence of dynamics on multiple time and temperature scales. Transiently elevating the temperature results in spontaneous chemical deamidation visible in the NMR spectra, which we validate using both capillary electrophoresis and mass spectrometry (MS) experiments. High temperatures also result in greatly accelerated intrinsic rates of hydrogen exchange and signal loss in NMR heteronuclear single quantum coherence spectra from local unfolding. These losses are in excellent agreement with both room temperature hydrogen exchange experiments and hydrogen bond disruption in replica exchange molecular dynamics simulations. Our analysis reveals important principles for future miniprotein designs and the potential for high stability to result in long‐lived alternate conformational states. [ABSTRACT FROM AUTHOR]

Many agricultural watersheds rely on the voluntary use of management practices (MPs) to reduce nonpoint source nutrient and sediment loads; however, the water‐quality effects of MPs are uncertain. We interpreted water‐quality responses from as early as 1985 through 2020 in three agricultural Chesapeake Bay watersheds that were prioritized for MP implementation, namely, the Smith Creek (Virginia), Upper Chester River (Maryland), and Conewago Creek (Pennsylvania) watersheds. We synthesized patterns in MPs, climate, land use, and nutrient inputs to better understand factors affecting nutrient and sediment loads. Relations between MPs and expected water‐quality improvements were not consistently identifiable. The number of MPs increased in all watersheds since the early 2010s, but most monitored nutrient and sediment loads did not decrease. Nutrient and sediment loads increased or remained stable in Smith Creek and the Upper Chester River. Sediment loads and some nutrient loads decreased in Conewago Creek. In Smith Creek, a 36‐year time‐series model suggests that changes in manure affected flow‐normalized total nitrogen loads. We hypothesize that increases in nutrient applications may overshadow some expected MP effects. MPs might have stemmed further water‐quality degradation, but improvements in nutrient loads may rely on reducing manure and fertilizer applications. Our results highlight the importance of assessing MP performance with long‐term monitoring‐based studies. [ABSTRACT FROM AUTHOR]

The attainment of the global target of zero dog‐mediated human rabies by 2030 depends on functional rabies programmes. Nigeria, a rabies‐endemic country, and the most populous country in Africa has a very poor rabies control strategy with a score of 1.5 out of 5 based on the Stepwise Approach towards Rabies Elimination (SARE). In this article, we report a scoping review that we conducted to highlight the strengths, weaknesses, opportunities and threats as well as situational analysis of rabies control in Nigeria and suggest a timeline for key activities that are needed to ensure zero by 30. Our findings reveal that rabies is grossly under‐reported as only 998 human and 273 dog‐suspected rabies cases were reported across Nigeria between 2017 and 2022. Our literature review also demonstrates a paucity of information on rabies in both human and animal health sectors. A total of 49 studies on dog rabies in Nigeria, with a predominance of reports from the North Central geopolitical region (48%, n = 23) were therefore included in this study. Currently, only 16.2% (n = 6/37) of Nigerian states have available data related to the estimated dog populations, the dog ownership rates, the vaccination status of dogs or the incidence of dog bites. Based on a dog‐to‐human ratio of 1:16.3, we estimated that the dog population in Nigeria was 12,969,368 (95% CI: 12,320,900‐13,617,836). Thus, to attain herd immunity and dog rabies control in Nigeria, at least 9.1 million dogs must be vaccinated annually. Our review reveals that, despite the strengths and available opportunities to achieve rabies control in Nigeria by 2030, the weaknesses and challenges will make the attainment of zero by 30 very difficult or impossible. Nigeria's best‐case scenario by the year 2030 is SARE stage 3–4 (control–elimination) out of 5. Otherwise, the rabies control programme might not surpass SARE stages 2–3. To attain zero by 30, Nigeria must re‐strategize its current rabies control programme by funding and implementing the national strategic plan for rabies control, creating a rabies desk office in the 37 states (FCT inclusive), rigorously conducting mass vaccination campaigns, providing post‐exposure prophylaxis, prioritizing mass enlightenment with a focus on responsible pet ownership and conduct baseline national rabies surveillance in the animal and human health sectors. [ABSTRACT FROM AUTHOR]