Hasil untuk "Veterinary medicine"

Dimar Sari Wahyuni, Komang Gede Wiryawan, Roni Ridwan et al.

Background and Aim: Enteric methane emissions from ruminants contribute significantly to greenhouse gas production, prompting research into sustainable feed additives. Macroalgae, rich in bioactive compounds, show promise in modulating rumen fermentation, improving digestibility, and reducing methane output. Indonesia’s diverse native macroalgae remain largely unexplored for these purposes, necessitating comparative evaluation to identify promising species for ruminant nutrition. This study aimed to conduct the first comparative in vitro evaluation of rumen fermentation patterns, digestibility characteristics, gas production kinetics, and enteric methane emissions using 14 native Indonesian macroalgae species, including brown (Sargassum sp., Padina sp., Turbinaria ornata), green (Boergesenia forbesii, Caulerpa racemosa, Ulva lactuca), and red (Palmaria palmata, Gelidium sp., Halymenia durvillei, Gracilaria verrucosa, Eucheuma cottonii, Gracilaria gigas, Eucheuma spinosum, Gracilaria coronopifolia) algae, to identify candidates for sustainable ruminant feed additives. Materials and Methods: Macroalgae samples were collected from various Indonesian locations, dried, and analyzed for chemical composition (dry matter, ash, crude protein, crude fat, crude fiber, nitrogen-free extract). In vitro fermentation was performed using a completely randomized design with five replicates per species. Samples (0.5 g) were incubated at 39°C for 72 h in buffered rumen fluid from fistulated Ongole crossbreed cattle. Parameters measured included total gas production, methane emissions (estimated via volatile fatty acid [VFA] profiles), ammonia, total and partial VFAs (acetate, propionate, butyrate, valerate, iso-butyrate, iso-valerate), acetate-to-propionate ratio, in vitro dry matter digestibility (IVDMD), in vitro organic matter digestibility (IVOMD), partitioning factor, microbial protein synthesis, and gas production kinetics. Data were analyzed using a one way analysis of variance with significance at p < 0.05 or p < 0.01, followed by post-hoc tests. Results: Chemical composition varied widely; red algae like Palmaria palmata had high crude protein (22.39 % dry matter), while brown algae like Padina sp. were ash-rich (74.39 % dry matter). Total gas production was highest in B. forbesii (54.75 mL; p < 0.01) and lowest in T. ornata (10.94 mL). Methane emissions and methane per incubated dry matter were lowest in Sargassum sp. (1.87 mM and 3.75 mM/g dry matter; p < 0.01), with Sargassum sp. and C. racemosa reducing methane by 71.86 %. Ammonia levels were similar across species (p > 0.05). Total VFA and propionate were highest in H. durvillei and B. forbesii (p < 0.01), with reduced acetate-to-propionate ratios. IVDMD and IVOMD were highest in H. durvillei (81.72 % and 69.53 %; p < 0.01). Gas kinetics showed B. forbesii with the highest asymptote (201.97 mL; p < 0.01) but slowest rate (0.01 mL/h). Positive correlations existed between crude protein and VFA/ammonia, while crude fiber inversely correlated with gas production and digestibility. Conclusion: H. durvillei emerged as optimal for enhancing rumen fermentation and digestibility, while Sargassum sp. excelled in methane mitigation. These species hold promise as natural additives for reducing environmental impacts in ruminant production, warranting in vivo validation for optimal inclusion rates and long-term effects.

Vaskar Chakma, MD Jaheid Hasan Nerab, Abdur Rouf et al.

Smoking continues to be a major preventable cause of death worldwide, affecting millions through damage to the heart, metabolism, liver, and kidneys. However, current medical screening methods often miss the early warning signs of smoking-related health problems, leading to late-stage diagnoses when treatment options become limited. This study presents a systematic comparative evaluation of machine learning approaches for smoking-related health risk assessment, emphasizing clinical interpretability and practical deployment over algorithmic innovation. We analyzed health screening data from 55,691 individuals, examining various health indicators, including body measurements, blood tests, and demographic information. We tested three advanced prediction algorithms - Random Forest, XGBoost, and LightGBM - to determine which could most accurately identify people at high risk. This study employed a cross-sectional design to classify current smoking status based on health screening biomarkers, not to predict future disease development. Our Random Forest model performed best, achieving an Area Under the Curve (AUC) of 0.926, meaning it could reliably distinguish between high-risk and lower-risk individuals. Using SHAP (SHapley Additive exPlanations) analysis to understand what the model was detecting, we found that key health markers played crucial roles in prediction: blood pressure levels, triglyceride concentrations, liver enzyme readings, and kidney function indicators (serum creatinine) were the strongest signals of declining health in smokers.

Mihaela Macarie-Ancau, Adrian Groza

Accurate classification of blood cells plays a key role in improving automated blood analysis for both medical and veterinary applications. This work presents a two-stage deep clustering method for classifying blood cells from high-dimensional signal data. In the first stage, red blood cells (RBCs) and platelets (PLTs) are separated using a combination of an improved autoencoder and the IDEC algorithm. The second stage further classifies RBC subtypes, pure RBCs, reticulocytes, and clumped RBCs, through a variational deep embedding (VaDE) approach. Due to the lack of detailed cell-level labels, soft classification probabilities are generated from sample-level data to approximate the true distributions. The aim is to contribute to the development of low-cost, automated blood analysis systems suitable for veterinary and biomedical use. Initial results indicate this method shows promise in effectively distinguishing different blood cell populations, even with limited supervision.

Geovane Rosa de Oliveira, Carla de Andrade, Celina Tie Nishimori Duque et al.

The use of nanofibers in farm animal diets can enhance nutrient absorption, minimize environmental problems, and generate a sustainable source of income. In this study, we investigated the effects of the partial inclusion of nanofibers produced from the pupunha heart of the palm sheath (nanopupunha) in the diet of growing New Zealand White rabbits on zootechnical performance, organ morphometry, digestive content pH, intestinal histology, biochemical and immunological parameters, and cecum microbiota. Twenty-four male and female New Zealand White rabbits were distributed into the control group fed a basal diet with 14% crude fiber and treatment groups with the basal diet supplemented with 3.5% or 10.5% of nanopupunha, according to their initial weight. After euthanasia on day 42, we analyzed the pH of the stomach contents, jejunum, and cecum, and the relative weights of the digestive tract, liver, kidneys, and spleen. Duodenal and jejunal samples were collected for structural and ultrastructural analyses of the intestinal villi. Additionally, blood samples were collected to analyze blood glucose, cholesterol, triglycerides, and immunological analysis (IgG and IgM), and digesta samples from the cecum were collected to count enterobacteria and lactic acid bacteria. The inclusion of dietary nanopupunha did not affect the zootechnical performance of animals, but resulted in a linear decrease in the relative weight of the stomach and a linear increase in the relative weight of the spleen. No significant differences were observed in the pH of the digestive tract. Nanopupunha inclusion also resulted in a linear increase in the crypt depth of the duodenum, total mucosal thickness, and total cholesterol levels in growing rabbits. Including 10.5% of nanopupunha added to the diet showed the best results in terms of the intestinal health of the growing rabbits.

Whenty Ariyanti, Kai-Chun Liu, Kuan-Yu Chen et al.

Respiratory disease, the third leading cause of deaths globally, is considered a high-priority ailment requiring significant research on identification and treatment. Stethoscope-recorded lung sounds and artificial intelligence-powered devices have been used to identify lung disorders and aid specialists in making accurate diagnoses. In this study, audio-spectrogram vision transformer (AS-ViT), a new approach for identifying abnormal respiration sounds, was developed. The sounds of the lungs are converted into visual representations called spectrograms using a technique called short-time Fourier transform (STFT). These images are then analyzed using a model called vision transformer to identify different types of respiratory sounds. The classification was carried out using the ICBHI 2017 database, which includes various types of lung sounds with different frequencies, noise levels, and backgrounds. The proposed AS-ViT method was evaluated using three metrics and achieved 79.1% and 59.8% for 60:40 split ratio and 86.4% and 69.3% for 80:20 split ratio in terms of unweighted average recall and overall scores respectively for respiratory sound detection, surpassing previous state-of-the-art results.

Hassam Khan Wazir, Zaid Waghoo, Vikram Kapila

Several therapy routines require deep breathing exercises as a key component and patients undergoing such therapies must perform these exercises regularly. Assessing the outcome of a therapy and tailoring its course necessitates monitoring a patient's compliance with the therapy. While therapy compliance monitoring is routine in a clinical environment, it is challenging to do in an at-home setting. This is so because a home setting lacks access to specialized equipment and skilled professionals needed to effectively monitor the performance of a therapy routine by a patient. For some types of therapies, these challenges can be addressed with the use of consumer-grade hardware, such as earphones and smartphones, as practical solutions. To accurately monitor breathing exercises using wireless earphones, this paper proposes a framework that has the potential for assessing a patient's compliance with an at-home therapy. The proposed system performs real-time detection of breathing phases and channels with high accuracy by processing a $\mathbf{500}$ ms audio signal through two convolutional neural networks. The first network, called a channel classifier, distinguishes between nasal and oral breathing, and a pause. The second network, called a phase classifier, determines whether the audio segment is from inhalation or exhalation. According to $k$-fold cross-validation, the channel and phase classifiers achieved a maximum F1 score of $\mathbf{97.99\%}$ and $\mathbf{89.46\%}$, respectively. The results demonstrate the potential of using commodity earphones for real-time breathing channel and phase detection for breathing therapy compliance monitoring.

Junfei Guo, Zhengxiao Zhang, Le Luo Guan et al.

Abstract Background Subacute ruminal acidosis (SARA) is a common metabolic disorder of high yielding dairy cows, and it is associated with dysbiosis of the rumen and gut microbiome and host inflammation. This study evaluated the impact of two postbiotics from Saccharomyces cerevisiae fermentation products (SCFP) on rumen liquid associated microbiota of lactating dairy cows subjected to repeated grain-based SARA challenges. A total of 32 rumen cannulated cows were randomly assigned to 4 treatments from 4 weeks before until 12 weeks after parturition. Treatment groups included a Control diet or diets supplemented with postbiotics (SCFPa, 14 g/d Original XPC; SCFPb-1X, 19 g/d NutriTek; SCFPb-2X, 38 g/d NutriTek, Diamond V, Cedar Rapids, IA, USA). Grain-based SARA challenges were conducted during week 5 (SARA1) and week 8 (SARA2) after parturition by replacing 20% DM of the base total mixed ration (TMR) with pellets containing 50% ground barley and 50% ground wheat. Total DNA from rumen liquid samples was subjected to V3–V4 16S rRNA gene amplicon sequencing. Characteristics of rumen microbiota were compared among treatments and SARA stages. Results Both SARA challenges reduced the diversity and richness of rumen liquid microbiota, altered the overall composition (β-diversity), and its predicted functionality including carbohydrates and amino acids metabolic pathways. The SARA challenges also reduced the number of significant associations among different taxa, number of hub taxa and their composition in the microbial co-occurrence networks. Supplementation with SCFP postbiotics, in particular SCFPb-2X, enhanced the robustness of the rumen microbiota. The SCFP supplemented cows had less fluctuation in relative abundances of community members when exposed to SARA challenges. The SCFP supplementation promoted the populations of lactate utilizing and fibrolytic bacteria, including members of Ruminococcaceae and Lachnospiraceae, and also increased the numbers of hub taxa during non-SARA and SARA stages. Supplementation with SCFPb-2X prevented the fluctuations in the abundances of hub taxa that were positively correlated with the acetate concentration, and α- and β-diversity metrics in rumen liquid digesta. Conclusions Induction of SARA challenges reduced microbiota richness and diversity and caused fluctuations in major bacterial phyla in rumen liquid microbiota in lactating dairy cows. Supplementation of SCFP postbiotics could attenuate adverse effects of SARA on rumen liquid microbiota.

Shuo Wei, Felix Kwame Amevor, Xiaxia Du et al.

Abstract Background Granulosa cell (GC) apoptosis, ferroptosis, and other programmed cell death processes are markers of follicular aging. Quercetin has been shown to reduce ferroptosis, however, its effects on ferroptosis in poultry remains unexplored. Our preliminary study identified ferroptosis in aging ovaries. Therefore, in the present study, 540-day-old Mountain Plum-blossom chickens were fed with quercetin supplementation at varying doses (0.2, 0.4, and 0.6 g/kg), and examined its molecular effects on GC ferroptosis using an in vitro Erastin-induced model. Results The results showed that quercetin supplementation significantly increased egg production, which confirmed its potential to alleviate ferroptosis in chicken ovarian tissue. The in vitro experiment revealed that quercetin and Fer-1 (positive control) mitigated Erastin-induced ferroptosis in GCs. Further, transcriptome analysis revealed that quercetin modulated key genes such as acyl-CoA synthetase long-chain family member 4 (ACSL4), solute carrier family 7 member 11 (SLC7A11), and transferrin receptor (TFRC), involved in ferroptosis regulation. The results further showed that quercetin also reduced Erastin-induced apoptosis and inflammation by modulating the expression of genes and proteins related to apoptosis and inflammatory factors (NF-κB, TNF-α, IL-6, and IL-10). Conclusion Taken together, the results showed that quercetin improves egg production performance in chickens and mitigates ovarian ferroptosis in aging hens, and inhibits Erastin-induced ferroptosis, inflammation, and apoptosis in GCs. These findings revealed the protective role of quercetin in poultry ovarian tissue and its cellular mechanisms against detrimental factors in poultry production. Graphical Abstract

Muhammad Hamza, Hafeez Ur Rehman Ali Khera, Muhammad Umair Waqas et al.

A survey was conducted from February 2022 to May 2022 on the usage of antibiotics at a poultry farm in different areas of Multan, Punjab Pakistan. A well-organized questionnaire was used for the collection of data. Sixty poultry farms were surveyed randomly in the Multan district. All of these Farms were using antibiotics. Antibiotics are commonly used for the treatment of diseases. Some are used as preventive medicine and a few are used as growth promotors. neomycin, erythromycin, oxytetracycline, streptomycin, and colistin are the broad-spectrum antibiotics that are being used commercially. Enrofloxacin and Furazolidone are the common antibiotics that are being used in Studies these days. The class of Fluoroquinolones is commonly used in poultry farms. Thirty-three patterns of antibiotic usage were observed at poultry farms. multi-drug practices were also observed on various farms. In this study, 25% of antibiotics are prescribed by the veterans while more than 90 % were acquired from the veterinary store. This study provides information about the antibiotics which are commonly being used in the study location district Multan. It is expected that the finding of this survey will be helpful in the development of new strategies against the misuse of antibiotics on farms.

Mohamed Abdelaziz Zaitri, Hanaa Zitane, Delfim F. M. Torres

We present a novel Pharmacokinetic/Pharmacodynamic (PK/PD) model for the induction phase of anesthesia, incorporating the $ψ$-Caputo fractional derivative. By employing the Picard iterative process, we derive a solution for a nonhomogeneous $ψ$-Caputo fractional system to characterize the dynamical behavior of the drugs distribution within a patient's body during the anesthesia process. To explore the dynamics of the fractional anesthesia model, we perform numerical analysis on solutions involving various functions of $ψ$ and fractional orders. All numerical simulations are conducted using the MATLAB computing environment. Our results suggest that the $ψ$ functions and the fractional order of differentiation have an important role in the modeling of individual-specific characteristics, taking into account the complex interplay between drug concentration and its effect on the human body. This innovative model serves to advance the understanding of personalized drug responses during anesthesia, paving the way for more precise and tailored approaches to anesthetic drug administration.

Mohsen JANMOHAMMADI, Maryam MOHAMADZADEH-ALGHOO, Naser SABAGHNIA et al.

Current study was conducted to evaluate the effects of different seed priming and foliar spray of micronutrients on bread wheat performance in semi-arid region in Northwest of Iran. Pre-sowing treatments were S1: no pre-sowing treatment (intact seeds), S2: hydro-priming, S3: bio-priming (seed inoculation with plant promoting rhizobacteria consortium: Azotobacter chroococcum + Azospirillum lipoferum), S4: micronutrient seed priming and foliar feeding include, check (0): distilled water spray, Fe: foliar spray of iron, Zn: foliar spray of zinc.  All seed priming treatments significantly increased plant height, tiller number, canopy width, total biomass, spike mass, seed number per spike and seed yield compared to intact seeds. A brief comparison of the effect of seed priming and fertilizer treatments showed that the effects of priming treatments on improving growth and seed yield was more obvious than fertilizer treatments. The greatest increase in seed yield and yield components was recorded for plants grown from bio-fortified seeds by essential trace elements. However, comparison of fertilizer treatments showed that growth parameters were significantly affected by Zn application. From the present study, it may be concluded that combined seed priming through pre-sowing hydration, soaking in micronutrients and microbial inoculation is useful to enhance wheat production and agricultural sustainability for smallholder farmers in semi-arid region.

Ahmed Ferchiou, Youba Ndiaye, Mostafa A. Mandour et al.

Maintaining udder health is the primary indication for antimicrobial use (AMU) in dairy production, and modulating this application is a key factor in decreasing AMU. Defining the optimal AMU and the associated practical rules is challenging since AMU interacts with many parameters. To define the trade-offs between decreased AMU, labor and economic performance, the bioeconomic stochastic simulation model DairyHealthSim (DHS)© was applied to dairy cow mastitis management and coupled to a mean variance optimization model and marginal abatement cost curve (MACC) analysis. The scenarios included three antimicrobial (AM) treatment strategies at dry-off, five types of general barn hygiene practices, five milking practices focused on parlor hygiene levels and three milk withdrawal strategies. The first part of economic results showed similar economic performances for the blanked dry-off strategy and selective strategy but demonstrated the trade-off between AMU reduction and farmers’ workload. The second part of the results demonstrated the optimal value of the animal level of exposure to AM (ALEA). The MACC analysis showed that reducing ALEA below 1.5 was associated with a EUR 10,000 loss per unit of ALEA on average for the farmer. The results call for more integrative farm decision processes and bioeconomic reasoning to prompt efficient public interventions.

Maria Butylina, Ursula Föger-Samwald, Katharina Gelles et al.

Hypoparathyroidism is a relatively rare human and veterinary disease characterized by deficient or absent production of parathyroid hormone (PTH). PTH is known as a classical regulator of calcium and phosphorus homeostasis. Nevertheless, the hormone also appears to modulate immune functions. For example, increased CD4:CD8 T-cell ratios and elevated interleukin (IL)-6 and IL-17A levels were observed in patients with hyperparathyroidism, whereas gene expression of tumor necrosis factor-α (TNF-α) and granulocyte macrophage-colony stimulating factor (GM-CSF) was decreased in patients with chronic postsurgical hypoparathyroidism. Various immune cell populations are affected differently. So, there is a need for validated animal models for the further characterization of this disease for identifying targeted immune-modulatory therapies. In addition to genetically modified mouse models of hypoparathyroidism, there are surgical rodent models. Parathyroidectomy (PTX) can be well performed in rats—for pharmacological and associated osteoimmunological research and bone mechanical studies, a large animal model could be preferable, however. A major drawback for successfully performing total PTX in large animal species (pigs and sheep) is the presence of accessory glands, thus demanding to develop new approaches for real-time detection of all parathyroid tissues.

Annisa Alifia, Sri Mulyati, Wurlina Wurlina et al.

This study aims to determine the effect of the administration of white guava (Psidium guajava L.) fruit juice on spermatozoa plasma membrane integrity (PMI), morphological abnormality, viability, and motility of lead acetate induced rats (Rattus norvegicus). Twenty-five male rats were divided into five groups: NC (negative control) group, rats were administered with distilled water twice daily at four-hour intervals; T0 (positive control) group, rats were administered daily with lead acetate 50 mg/kg bw and distilled water four hours later; T1, T2, and T3 groups, rats were administered daily with lead acetate 50 mg/kg bw and 0.5 mL of 25, 50, and 100% white guava fruit juice four hours later. The treatment of the rats was conducted for 14 days, and on day 15, all rats were sacrificed to assess the spermatozoa quality. Data was analyzed using ANOVA followed by Duncan's multiple range test at a confidence level of 95%. The results showed that exposure to lead acetate (T0) caused lower spermatozoa PMI, viability, and motility as well as higher spermatozoa morphological abnormalities (p <0.05) compared to those of the T0 group. Administration of white guava fruit juice starting at a dose of 25% (T1) resulted in higher spermatozoa motility, viability, and PMI as well as lower spermatozoa morphological abnormalities (p <0.05) compared to rats in the T0 group. It could be concluded that white guava fruit juice maintained the spermatozoa quality of lead acetate induced rats.

Michael Daniels, Adam Smith, Conceicao Bettencourt et al.

Background Effective development and retention of talented early-career researchers (ECRs) is essential to the continued success of biomedical science research fields. To this end, formal mentorship programmes (where researchers are paired with one or more mentors beyond their direct manager) have proven to be successful in providing support and expanding career development opportunities. However, many programmes are limited to pools of mentors and mentees within one institute or geographical area, highlighting that cross-regional connections may be a missed opportunity in many mentorship schemes. Methods Here, we aimed to address this limitation through our pilot cross-regional mentorship scheme, creating reciprocal mentor-mentee pairings between two pre-established networks of Alzheimer’s Research UK (ARUK) Network-associated researchers. We carefully created 21 mentor-mentee pairings between the Scotland and University College London (UCL) networks in 2021, with surveys conducted to assess mentor/mentee satisfaction with the programme. Results Participants reported very high satisfaction with the nature of the pairings and the mentors’ contribution to the career development of mentees; a majority also reported that the mentorship scheme increased their connections outside of their home network. Our assessment of this pilot programme is that it supports the utility of cross-regional mentorship schemes for ECR development. At the same time, we highlight the limitations of our programme and recommend areas for improvement in future programmes, including greater consideration of support for minoritized groups and the need for additional training for mentors. Conclusions In conclusion, our pilot scheme generated successful and novel mentor-mentee pairings across pre-existing networks; both of which reported high satisfaction with pairings, ECR career and personal development, and the formation of new cross-network connections. This pilot may serve as a model for other networks of biomedical researchers, where existing networks within medical research charities can act as a scaffold to build new cross-regional career development opportunities for researchers.

Chenxin Han, Chenxin Han, Ziwei Xie et al.

IntroductionGlucose Regulated Proteins/Binding protein (GRP78/Bip), a representative molecular chaperone, effectively influences and actively participates in the replication processes of many viruses. Little is known, however, about the functional involvement of GRP78 in the replication of Newcastle disease virus (NDV) and the underlying mechanisms.MethodsThe method of this study are to establish protein interactomes between host cell proteins and the NDV Hemagglutinin-neuraminidase (HN) protein, and to systematically investigate the regulatory role of the GRP78-HN protein interaction during the NDV replication cycle.ResultsOur study revealed that GRP78 is upregulated during NDV infection, and its direct interaction with HN is mediated by the N-terminal 326 amino acid region. Knockdown of GRP78 by small interfering RNAs (siRNAs) significantly suppressed NDV infection and replication. Conversely, overexpression of GRP78 resulted in a significant increase in NDV replication, demonstrating its role as a positive regulator in the NDV replication cycle. We further showed that the direct interaction between GRP78 and HN protein enhanced the attachment of NDV to cells, and masking of GRP78 expressed on the cell surface with specific polyclonal antibodies (pAbs) inhibited NDV attachment and replication.DiscussionThese findings highlight the essential role of GRP78 in the adsorption stage during the NDV infection cycle, and, importantly, identify the critical domain required for GRP78-HN interaction, providing novel insights into the molecular mechanisms involved in NDV replication and infection.

Nilgun Sengoz, Tuncay Yigit, Ozlem Ozmen et al.

The aim of this study is to propose an alternative and hybrid solution method for diagnosing the disease from histopathology images taken from animals with paratuberculosis and intact intestine. In detail, the hybrid method is based on using both image processing and deep learning for better results. Reliable disease detection from histo-pathology images is known as an open problem in medical image processing and alternative solutions need to be developed. In this context, 520 histopathology images were collected in a joint study with Burdur Mehmet Akif Ersoy University, Faculty of Veterinary Medicine, and Department of Pathology. Manually detecting and interpreting these images requires expertise and a lot of processing time. For this reason, veterinarians, especially newly recruited physicians, have a great need for imaging and computer vision systems in the development of detection and treatment methods for this disease. The proposed solution method in this study is to use the CLAHE method and image processing together. After this preprocessing, the diagnosis is made by classifying a convolutional neural network sup-ported by the VGG-16 architecture. This method uses completely original dataset images. Two types of systems were applied for the evaluation parameters. While the F1 Score was 93% in the method classified without data preprocessing, it was 98% in the method that was preprocessed with the CLAHE method.

Conor Moloney, Antonella Puggioni, Myles McKenna

Abstract A 9‐week‐old male intact Cavalier King Charles Spaniel was presented for evaluation of acute onset dyspnea caused by left‐sided pneumothorax. Thoracic computed tomography (CT) identified multiple pulmonary bullae and blebs in multiple lung lobes. Rupture of ≥1 pulmonary blebs or bullae, precipitated by low impact trauma, was the suspected cause of pneumothorax. A volume of 7.5 mL/kg of fresh whole blood was collected from a type‐matched donor dog and administered into the left pleural space using a thoracostomy tube. The pneumothorax was successfully resolved and no adverse effects of blood patch pleurodesis were noted. The dog was clinically normal 12 months later.

Peisheng Qian, Ziyuan Zhao, Cong Chen et al.

Diabetic retinopathy (DR) is one of the most common eye conditions among diabetic patients. However, vision loss occurs primarily in the late stages of DR, and the symptoms of visual impairment, ranging from mild to severe, can vary greatly, adding to the burden of diagnosis and treatment in clinical practice. Deep learning methods based on retinal images have achieved remarkable success in automatic DR grading, but most of them neglect that the presence of diabetes usually affects both eyes, and ophthalmologists usually compare both eyes concurrently for DR diagnosis, leaving correlations between left and right eyes unexploited. In this study, simulating the diagnostic process, we propose a two-stream binocular network to capture the subtle correlations between left and right eyes, in which, paired images of eyes are fed into two identical subnetworks separately during training. We design a contrastive grading loss to learn binocular correlation for five-class DR detection, which maximizes inter-class dissimilarity while minimizing the intra-class difference. Experimental results on the EyePACS dataset show the superiority of the proposed binocular model, outperforming monocular methods by a large margin.

Matthias Wödlinger, Michael Reiter, Lisa Weijler et al.

Acute Lymphoblastic Leukemia (ALL) is the most frequent hematologic malignancy in children and adolescents. A strong prognostic factor in ALL is given by the Minimal Residual Disease (MRD), which is a measure for the number of leukemic cells persistent in a patient. Manual MRD assessment from Multiparameter Flow Cytometry (FCM) data after treatment is time-consuming and subjective. In this work, we present an automated method to compute the MRD value directly from FCM data. We present a novel neural network approach based on the transformer architecture that learns to directly identify blast cells in a sample. We train our method in a supervised manner and evaluate it on publicly available ALL FCM data from three different clinical centers. Our method reaches a median F1 score of ~0.94 when evaluated on 519 B-ALL samples and shows better results than existing methods on 4 different datasets