scDM: A deep generative method for cell surface protein prediction with diffusion model.

The executors of organismal functions are proteins, and the transition from RNA to protein is subject to post-transcriptional regulation; therefore, considering both RNA and surface protein expression simultaneously can provide additional evidence of biological processes. Cellular indexing of transcriptomes and epitopes by sequencing (CITE-Seq) technology can measure both RNA and protein expression in single cells, but these experiments are expensive and time-consuming. Due to the lack of computational tools for predicting surface proteins, we used datasets obtained with CITE-seq technology to design a deep generative prediction method based on diffusion models and to find biological discoveries through the prediction results. In our method, the scDM, which predicts protein expression values from RNA expression values of individual cells, uses a novel way of encoding the data into a model and generates predicted samples by introducing Gaussian noise to gradually remove the noise to learn the data distribution during the modelling process. Comprehensive evaluation across different datasets demonstrated that our predictions yielded satisfactory results and further demonstrated the effectiveness of incorporating information from single-cell multiomics data into diffusion models for biological studies. We also found that new directions for discovering therapeutic drug targets could be provided by jointly analysing the predictive value of surface protein expression and cancer cell drug scores.

Aluminum Halide-Based Electron-Selective Passivating Contacts for Crystalline Silicon Solar Cells.

Extensive research has focused on developing wide-bandgap metal compound-based passivating contacts as alternatives to conventional doped-silicon-layer-based passivating contacts to mitigate parasitic absorption losses in crystalline silicon (c-Si) solar cells. Herein, thermally-evaporated aluminum halides (AlX)-based electron-selective passivating contacts for c-Si solar cells are investigated. A low contact resistivity of 60.5 and 38.4 mΩ cm2 is obtained on the AlClx /n-type c-Si (n-Si) and AlFx /n-Si heterocontacts, respectively, thanks to the low work function of AlX. Power conversion efficiencies (PCEs) of 19.1% and 19.6% are achieved on proof-of-concept n-Si solar cells featuring a full-area AlClx /Al and AlFx /Al passivating contact, respectively. By further implementing an ultrathin SiO2 passivation interlayer and a pre-annealing treatment, the electron selectivity (especially the surface passivation) of AlX is significantly enhanced. Accordingly, a remarkable PCE of 21% is achieved on n-Si solar cells featuring a full-area SiO2 /AlFx /Al rear contact. AlFx -based electron-selective passivating contacts exhibit good thermal stability up to ≈400 °C and better long-term environmental stability. This work demonstrates the potential of AlFx -based electron-selective passivating contact for solar cells.

A general platform for targeting MHC-II antigens via a single loop.

Class-II major histocompatibility complexes (MHC-IIs) are central to the communications between CD4+ T cells and antigen presenting cells (APCs), but intrinsic structural features associated with MHC-II make it difficult to develop a general targeting system with high affinity and antigen specificity. Here, we introduce a protein platform, Targeted Recognition of Antigen-MHC Complex Reporter for MHC-II (TRACeR-II), to enable the rapid development of peptide-specific MHC-II binders. TRACeR-II has a small helical bundle scaffold and uses an unconventional mechanism to recognize antigens via a single loop. This unique antigen-recognition mechanism renders this platform highly versatile and amenable to direct structural modeling of the interactions with the antigen. We demonstrate that TRACeR-II binders can be rapidly evolved across multiple alleles, while computational protein design can produce specific binding sequences for a SARS-CoV-2 peptide of unknown complex structure. TRACeR-II sheds light on a simple and straightforward approach to address the MHC peptide targeting challenge, without relying on combinatorial selection on complementarity determining region (CDR) loops. It presents a promising basis for further exploration in immune response modulation as well as a broad range of theragnostic applications.

Global prevalence and risk factors of Enterocytozoon bieneusi infection in humans: a systematic review and meta-analysis.

Enterocytozoon bieneusi is one of the most important zoonotic pathogens. In this study, we present a systematic review and meta-analysis of the prevalence of human E. bieneusi infection in endemic regions and analyze the various potential risk factors. A total of 75 studies were included. Among 31,644 individuals tested, 2,291 (6.59%) were E. bieneusi-positive. The highest prevalence of E. bieneusi in the male population was 5.50%. The prevalence of E. bieneusi in different age groups was varied, with 10.97% in teenagers. The prevalence of E. bieneusi in asymptomatic patients (6.49%) is significantly lower than that in HIV-infected patients (11.49%), and in patients with diarrheal symptoms (16.45%). Rural areas had a higher rate (7.58%) than urban ones. The prevalence of E. bieneusi in humans was the highest (6.42%) at altitudes <10 m. Moreover, the temperate zone marine climate (13.55%) had the highest prevalence. A total of 69 genotypes of E. bieneusi have been found in humans. This is the first global study regarding E. bieneusi prevalence in humans. Not only people with low immunity (such as the elderly, children, people with HIV, etc.), but also people in Europe in temperate marine climates should exercise caution to prevent infection with E. bieneusi during contact process with animals.

Title: Prévalence mondiale et facteurs de risque de l'infection à Enterocytozoon bieneusi chez l'homme : revue systématique et méta-analyse. Abstract: Enterocytozoon bieneusi est l'un des agents pathogènes zoonotiques les plus importants. Dans cette étude, nous présentons une revue systématique et une méta-analyse de la prévalence de l'infection humaine à E. bieneusi dans les régions endémiques et analysons les différents facteurs de risque potentiels. Au total, 75 études ont été incluses. Parmi 31 644 individus, 2 291 (6,59 %) étaient positifs à E. bieneusi. La prévalence la plus élevée d'E. bieneusi dans la population masculine était de 5,50 %. La prévalence d'E. bieneusi dans différents groupes d'âge variait, avec 10,97 % chez les adolescents. La prévalence d'E. bieneusi chez les patients asymptomatiques (6,49 %) était significativement inférieure à celle des patients VIH (11,49 %) et des patients présentant des symptômes de diarrhée (16,45 %). Les zones rurales avaient un taux plus élevé (7,58 %) que les zones urbaines. La prévalence d'E. bieneusi chez les humains était la plus élevée (6,42 %) à une altitude <10 m. De plus, le climat marin de la zone tempérée (13,55 %) avait la prévalence la plus élevée. Au total, 69 génotypes d'E. bieneusi ont été trouvés chez l'homme. Il s'agit de la première étude mondiale concernant la prévalence d'E. bieneusi chez l'homme. Non seulement les personnes ayant une faible immunité (telles que les personnes âgées, les enfants, les patients atteints du VIH, etc.), mais également les personnes vivant en Europe dans un climat marin tempéré doivent veiller à prévenir l'infection par E. bieneusi lors du contact avec des animaux.

A novel transformer-based aggregation model for predicting gene mutations in lung adenocarcinoma.

In recent years, predicting gene mutations on whole slide imaging (WSI) has gained prominence. The primary challenge is extracting global information and achieving unbiased semantic aggregation. To address this challenge, we propose a novel Transformer-based aggregation model, employing a self-learning weight aggregation mechanism to mitigate semantic bias caused by the abundance of features in WSI. Additionally, we adopt a random patch training method, which enhances model learning richness by randomly extracting feature vectors from WSI, thus addressing the issue of limited data. To demonstrate the model's effectiveness in predicting gene mutations, we leverage the lung adenocarcinoma dataset from Shandong Provincial Hospital for prior knowledge learning. Subsequently, we assess TP53, CSMD3, LRP1B, and TTN gene mutations using lung adenocarcinoma tissue pathology images and clinical data from The Cancer Genome Atlas (TCGA). The results indicate a notable increase in the AUC (Area Under the ROC Curve) value, averaging 4%, attesting to the model's performance improvement. Our research offers an efficient model to explore the correlation between pathological image features and molecular characteristics in lung adenocarcinoma patients. This model introduces a novel approach to clinical genetic testing, expected to enhance the efficiency of identifying molecular features and genetic testing in lung adenocarcinoma patients, ultimately providing more accurate and reliable results for related studies.

Semi-supervised breast cancer pathology image segmentation based on fine-grained classification guidance.

Breast cancer pathological image segmentation (BCPIS) holds significant value in assisting physicians with quantifying tumor regions and providing treatment guidance. However, achieving fine-grained semantic segmentation remains a major challenge for this technology. The complex and diverse morphologies of breast cancer tissue structures result in high costs for manual annotation, thereby limiting the sample size and annotation quality of the dataset. These practical issues have a significant impact on the segmentation performance. To overcome these challenges, this study proposes a semi-supervised learning model based on classification-guided segmentation. The model first utilizes a multi-scale convolutional network to extract rich semantic information and then employs a multi-expert cross-layer joint learning strategy, integrating a small number of labeled samples to iteratively provide the model with class-generated multi-cue pseudo-labels and real labels. Given the complexity of the breast cancer samples and the limited sample quantity, an innovative approach of augmenting additional unlabeled data was adopted to overcome this limitation. Experimental results demonstrate that, although the proposed model falls slightly behind supervised segmentation models, it still exhibits significant progress and innovation. The semi-supervised model in this study achieves outstanding performance, with an IoU (Intersection over Union) value of 71.53%. Compared to other semi-supervised methods, the model developed in this study demonstrates a performance advantage of approximately 3%. Furthermore, the research findings indicate a significant correlation between the classification and segmentation tasks in breast cancer pathological images, and the guidance of a multi-expert system can significantly enhance the fine-grained effects of semi-supervised semantic segmentation.

Microbacterium spp. peritonitis in patients undergoing peritoneal dialysis: a single-center experience and literature review.

Introduction: Peritoneal dialysis-related peritonitis (PDRP) caused by Microbacterium spp. is very rare, with only 9 cases reported to date. In this study, we report the treatment experiences of 7 patients at our peritoneal dialysis center. Methods: We retrospectively collected clinical characteristics and antibiotic management of all 7 episodes of PDRP caused by Microbacterium spp. in 7 patients from at our center over 4 years, and reviewed the documented Microbacterium spp. PDRP in the literature. Results: Empiric antibiotic therapy was initiated as soon as possible, and consisted of intraperitoneal (IP) gentamicin in combination with vancomycin. After up to 5 days, gentamicin was changed to meropenem if the treatment was not effective. The intended course of antibiotic treatment was 21-day. Totally, 6 episodes were cured (85.7%), which was higher than reported. Conclusion: The 21-day antibiotic therapy program by combining vancomycin and meropenem may benefit the management of Microbacterium spp. PDRP.

Can Catenated Nitrogen Compounds with Amine-like Structures Become Candidates for High-Energy-Density Compounds?

The worthwhile idea of whether amine-like catenated nitrogen compounds are stable enough to be used as high-energy materials was proposed and answered. Abstracting the NH3 structure into NR3 (R is the substituent) yields a new class of amine-like catenated nitrogen compounds. Most of the azole ring structures have a high nitrogen content and stability. Inspired by this idea, a series of new amine-like catenated nitrogen compounds (A1 to H5) were designed, and their basic energetic properties were calculated. The results showed that (1) amine-like molecular structures are often characterized by low density; however, the density of these compounds increases as the number of nitrogens in the azole ring increases; (2) these catenated nitrogen compounds generally have extremely high enthalpies of formation (882.91-2652.03 kJ/mol), and the detonation velocity of some compounds exceeds 9254.00 m/s; (3) the detonation performance of amine-like catenated nitrogen compounds designed based on imidazole and pyrazole rings is poor due to their low nitrogen content; and (4) the bond dissociation enthalpy of trigger bonds of most compounds is higher than 84 kJ/mol, indicating that these compounds have a certain thermodynamic stability. In summary, amine-like catenated nitrogen compounds have the potential to become energetic compounds with excellent detonation properties and should be considered to be synthesized by experimental chemists.

The Prevalence of Rodents Orientia tsutsugamushi in China During Two Decades: A Systematic Review and Meta-Analysis.

Background: Orientia tsutsugamushi is a zoonotic intracellular pathogen that requires parasitism in eukaryotic cells to reproduce. In recent years, tsutsugamushi disease reported in many places nationwide has crossed the Yangtze River, continuously, spreading to the North China. Now this phenomenon has aroused people's attention. Materials and Methods: In this study, meta-analysis was used to analyze the infection of rodents (vectors) in China, to clarify the transmission rule of O. tsutsugamushi. Results: This study included literature from six databases (PubMed, Web of Science, Science Direct, Wanfang, CNKI, and VIP). A total of 55 articles were included in the study from 610 retrieved articles. The total infection rate of O. tsutsugamushi in rodents was 5.5% (1206/20,620, 95% confidence interval [CI]: 0.0553-0.0617). The prevalence of O. tsutsugamushi in rodents before 2013 (7.73%, 95% CI: 4.11-12.37) was higher than after 2013 (2.11%, 95% CI: 0.64-4.41). O. tsutsugamushi spread among a variety of rodents, among which Rattus losea (13.3%, 95% CI: 4.33-26.26), Rattus tanezumi (5.69%, 95% CI: 1.37-12.72), and Apodemus agrarius (5.32%, 95% CI: 2.26-9.58) infection rate was higher. Kawasaki (8.32%, 95% CI: 1.42-20.17), Karp (7.36%, 95% CI: 2.62-14.22), Kato (2.54%, 95% CI: 0.08-8.28), and Gilliam (2.13%, 95% CI: 0.42-5.09) were the main prevalent genotypes in China. The prevalence of O. tsutsugamushi in rodents was seasonal, increasing gradually in summer (2.39%, 95% CI: 0.46-5.77), peaking in autumn (4.59%, 95% CI: 1.15-10.16), and then declining. The positive rate of immunofluorescence assay (25.07%, 95% CI: 8.44-46.88) was the highest among the detection methods, and it was statistically significant (p < 0.05). Based on the subgroup of geographical factors and climatic factors, the probability of O. tsutsugamushi infection in rodents was the highest when the temperature >19℃ (8.20%, 95% CI: 1.22-20.52), the altitude <100 millimeters (7.23%, 95% CI: 3.45-12.26), the precipitation >700 millimeters (12.22%, 95% CI: 6.45-19.50), and the humidity 60-70% (7.80%, 95% CI: 4.17-12.44). Conclusions: Studies have shown that rodents carrying O. tsutsugamushi are common. People should prevent and control rodents in life and monitor rodents carrying O. tsutsugamushi for a long time.

Using optimal transport theory to optimize a deep convolutional neural network microscopic cell counting method.

Medical image processing has become increasingly important in recent years, particularly in the field of microscopic cell imaging. However, accurately counting the number of cells in an image can be a challenging task due to the significant variations in cell size and shape. To tackle this problem, many existing methods rely on deep learning techniques, such as convolutional neural networks (CNNs), to count cells in an image or use regression counting methods to learn the similarities between an input image and a predicted cell image density map. In this paper, we propose a novel approach to monitor the cell counting process by optimizing the loss function using the optimal transport method, a rigorous measure to calculate the difference between the predicted count map and the dot annotation map generated by the CNN. We evaluated our algorithm on three publicly available cell count benchmarks: the synthetic fluorescence microscopy (VGG) dataset, the modified bone marrow (MBM) dataset, and the human subcutaneous adipose tissue (ADI) dataset. Our method outperforms other state-of-the-art methods, achieving a mean absolute error (MAE) of 2.3, 4.8, and 13.1 on the VGG, MBM, and ADI datasets, respectively, with smaller standard deviations. By using the optimal transport method, our approach provides a more accurate and reliable cell counting method for medical image processing.

Thermal stability of azole-rich energetic compounds: their structure, density, enthalpy of formation and energetic properties.

Energetic compounds, as a type of special material, are widely used in the fields of national defense, aerospace and exploration. Their research and production have received growing attention. Thermal stability is a crucial factor for the safety of energetic materials. Azole-rich energetic compounds have emerged as a research hotspot in recent years owing to their excellent properties. Due to the aromaticity of unsaturated azoles, many azole-rich energetic compounds have significant thermal stability, which is one of the properties that researchers focus on. This review presents a comprehensive summary of the physicochemical and energetic properties of various energetic materials, highlighting the relationship between thermal stability and the structural, physicochemical, and energetic properties of azole-rich energetic compounds. To improve the thermal stability of compounds, five aspects can be considered, including functional group modification, bridging, preparation of energetic salts, energetic metal-organic frameworks (EMOFs) and co-crystals. It was demonstrated that increasing the strength and number of hydrogen bonds of azoles and expanding the π-π stacking area are the key factors to improve thermal stability, which provides a valuable way to develop energetic materials with higher energy and thermal stability.

The Design, Synthesis and Application of Nitrogen Heteropolycyclic Compounds with UV Resistance Properties.

Exposure to ultraviolet (UV) light is known to cause skin aging, skin damage, cancer, and eye diseases, as well as polymer material aging. Therefore, significant attention has been devoted to the research and development of UV absorbers. Considering the robust hydrogen bonding and conjugated structure present in nitrogen-containing polycyclic compounds, these compounds have been selected as potential candidates for exploring ultraviolet absorption properties. After structural optimization and the simulation of ultraviolet absorption spectra, four tris-[1,2,4]-triazolo-[1,3,5]-triazine (TTTs) derivatives, namely TTTB, TTTD, TTTJ, and TTTL, were selected as the preferred compounds and synthesized. The structure of the compound was determined using various analytical techniques, including FTIR, 1HNMR, 13CNMR, HRMS, and XRD. Subsequently, composite films of polyvinyl chloride (PVC) and TTTs were produced using a simple solvent casting technique. The PVC films were subjected to UV age testing by exposing them to an ultraviolet aging chamber. The age-resistant performance of the fabricated films was evaluated using an ultraviolet spectrophotometer and Fourier infrared spectrum instrument. The findings suggest that TTTs exhibit a noteworthy capacity for absorbing ultraviolet radiation. The TTTL compound exhibits a superior UV absorption performance compared to commercially available UV absorbers such as UV-0 and UV-327 in the market.

Flexible solar cells based on foldable silicon wafers with blunted edges.

Flexible solar cells have a lot of market potential for application in photovoltaics integrated into buildings and wearable electronics because they are lightweight, shockproof and self-powered. Silicon solar cells have been successfully used in large power plants. However, despite the efforts made for more than 50 years, there has been no notable progress in the development of flexible silicon solar cells because of their rigidity1-4. Here we provide a strategy for fabricating large-scale, foldable silicon wafers and manufacturing flexible solar cells. A textured crystalline silicon wafer always starts to crack at the sharp channels between surface pyramids in the marginal region of the wafer. This fact enabled us to improve the flexibility of silicon wafers by blunting the pyramidal structure in the marginal regions. This edge-blunting technique enables commercial production of large-scale (>240 cm2), high-efficiency (>24%) silicon solar cells that can be rolled similarly to a sheet of paper. The cells retain 100% of their power conversion efficiency after 1,000 side-to-side bending cycles. After being assembled into large (>10,000 cm2) flexible modules, these cells retain 99.62% of their power after thermal cycling between -70 °C and 85 °C for 120 h. Furthermore, they retain 96.03% of their power after 20 min of exposure to air flow when attached to a soft gasbag, which models wind blowing during a violent storm.

A systematic review and meta-analysis of the seroconversion rates and adverse effects of COVID-19 mRNA vaccine and COVID-19 viral vector vaccine in kidney transplant recipient patients.

Patients received kidney transplantation (KTR) have a low seroconversion rate after vaccination. Our objective was to compare the seroconversion rates and adverse effects of additional different vaccinations in KTR patients in existing studies. Databases such as PubMed, Cochrane Library, Web of Science, Embase, ClinicalTrials.gov and others. Three high-quality RCT were included and showed no statistical difference in seroconversion rates between the two vaccines (RR = 0.93[0.76,1.13]). There was no statistical difference in seroconversion rates between the sexes, for men (RR = 0.93[0.69,1.25]) and women (RR = 0.91[0.62,1.33]). Among the adverse effects there was no statistically significant difference in fever (RR = 1.06[0.44,2.57]), while for injection site pain there was a statistically significant difference (RR = 1.14[1.18,1.84]). There was no significant difference in seroconversion rates in patients with KTR who received the two additional vaccines. Patients injected with the viral vector vaccine were less painful than those injected with the mRNA vaccine.

Revealing Surface and Interface Evolution of Molybdenum Nitride as Carrier-Selective Contacts for Crystalline Silicon Solar Cells.

Molybdenum nitride (MoNx) was perceived as carrier-selective contacts (CSCs) for crystalline silicon (c-Si) solar cells due to having proper work functions and excellent conductivities. However, the poor passivation and non-Ohmic contact at the c-Si/MoNx interface endow an inferior hole selectivity. Here, the surface, interface, and bulk structures of MoNx films are systematically investigated by X-ray scattering, surface spectroscopy, and electron microscope analysis to reveal the carrier-selective features. Surface layers with the composition of MoO2.51N0.21 form upon air exposure, which induces the overestimated work function and explains the origin of inferior hole selectivities. The c-Si/MoNx interface is confirmed to adopt long-term stability, providing guidance for designing stable CSCs. A detailed evolution of the scattering length density, domain sizes, and crystallinity in the bulk phase is presented to elucidate its superior conductivity. These multiscale structural investigations offer a clear structure-function correlation of MoNx films, providing key inspiration for developing excellent CSCs for c-Si solar cells.

Synthesis and characterization of potential polycyclic energetic materials using bicyclic triazole and azetidine structures as building blocks.

Exploring the design strategy of new energetic materials is crucial to promote the development of energetic materials. In this study, a method for designing polycyclic energetic materials is proposed by combining the azetidine structure with azobis-1,2,4-triazole or bi-1,2,4-triazole. A series of typical triazolyl polycyclic compounds were designed and synthesized by simple nucleophilic reaction, which included 5,5'-dichloro-3,3'-bis(3,3'-difluoroazetidine)-4,4'-azobis-1,2,4-triazole (1), 5,5'-dichloro-3,3'-bis(3,3'-difluoroazetidine)-4,4'-bi-1,2,4-triazole (2), 5,5'-dichloro-3-(N,N-dimethyl)-3'-(3,3'-difluoroazetidine)-4,4'-bi-1,2,4-triazole (3) 5,5'-dichloro-3,3'-bis(3,3'-dinitroazetidine)-4,4'-bi-1,2,4-triazole (4), 5,5'-dichloro-3-(N,N-dimethyl)-3'-(3,3'-dinitroazetidine)-4,4'-bi-1,2,4-triazole (5), and 5,5'-diazido-3,3'-bis(3,3'-difluoroazetidine)-4,4'-azo-1,2,4-triazole (6). These designed and synthesized polycyclic compounds (1, 2, 3) have high decomposition temperatures (>200 °C). The molecular van der Waals surface electrostatic potentials suggested the reactivity of compounds 1, 2, and 3 when attacked by nucleophiles. The natural bond orbital and Hirshfeld surface analysis proved the essential reason for the stability of these compounds in theory. The formula design example suggests that some triazolyl polycyclic compounds (4, 5, and 6) are potentially explosives, suggesting that this strategy is feasible for constructing the triazolyl polycyclic energetic compounds.

Insensitive High-Energy Density Materials Based on Azazole-Rich Rings: 1,2,4-Triazole N-Oxide Derivatives Containing Isomerized Nitro and Amino Groups.

It is an arduous and meaningful challenge to design and develop new energetic materials with lower sensitivity and higher energy. How to skillfully combine the characteristics of low sensitivity and high energy is the key problem in designing new insensitive high-energy materials. Taking a triazole ring as a framework, a strategy of N-oxide derivatives containing isomerized nitro and amino groups was proposed to answer this question. Based on this strategy, some 1,2,4-triazole N-oxide derivatives (NATNOs) were designed and explored. The electronic structure calculation showed that the stable existence of these triazole derivatives was due to the intramolecular hydrogen bond and other interactions. The impact sensitivity and the dissociation enthalpy of trigger bonds directly indicated that some compounds could exist stably. The crystal densities of all NATNOs were larger than 1.80 g/cm3, which met the requirement of high-energetic materials for crystal density. Some NATNOs (9748 m/s for NATNO, 9841 m/s for NATNO-1, 9818 m/s for NATNO-2, 9906 m/s for NATNO-3, and 9592 m/s for NATNO-4) were potential high detonation velocity energy materials. These study results not only indicate that the NATNOs have relatively stable properties and excellent detonation properties but also prove that the strategy of nitro amino position isomerization coupled with N-oxide is an effective means to develop new energetic materials.

Facile repurposing of peptide-MHC-restricted antibodies for cancer immunotherapy.

Monoclonal antibodies (Abs) that recognize major histocompatability complex (MHC)-presented tumor antigens in a manner similar to T cell receptors (TCRs) have great potential as cancer immunotherapeutics. However, isolation of 'TCR-mimic' (TCRm) Abs is laborious because Abs have not evolved the structurally nuanced peptide-MHC restriction of αß-TCRs. Here, we present a strategy for rapid isolation of highly peptide-specific and 'MHC-restricted' Abs by re-engineering preselected Abs that engage peptide-MHC in a manner structurally similar to that of conventional αß-TCRs. We created structure-based libraries focused on the peptide-interacting residues of TCRm Ab complementarity-determining region (CDR) loops, and rapidly generated MHC-restricted Abs to both mouse and human tumor antigens that specifically killed target cells when formatted as IgG, bispecific T cell engager (BiTE) and chimeric antigen receptor-T (CAR-T). Crystallographic analysis of one selected pMHC-restricted Ab revealed highly peptide-specific recognition, validating the engineering strategy. This approach can yield tumor antigen-specific antibodies in several weeks, potentially enabling rapid clinical translation.

Meta-Analysis of the Prevalence of Giardia duodenalis in Cattle in China.

Giardia duodenum (G. duodenalis) can cause giardiasis and infect a variety of hosts. So far, there have been no detailed data regarding the positive rate of G. duodenalis in cattle in China. Here, a systematic literature review was carried out to investigate the epidemiology of bovine G. duodenalis in China. To perform the meta-analysis, the databases China National Knowledge Infrastructure, VIP Chinese Journal Databases, WanFang Databases, PubMed, and ScienceDirect were employed for screening studies related to the prevalence of G. duodenalis in cattle in China. The total prevalence of G. duodenalis in cattle was estimated to be 8.00% (95% confidence interval [CI]: 5.51-11.62). In the age subgroup, the prevalence of G. duodenalis in calves (11.72%; 95% CI: 7.75-17.73) was significantly higher than that in cattle of other age groups. An analysis based on seasons showed that the prevalence of G. duodenalis in cattle was higher in summer (9.69%; 95% CI: 2.66-35.30) than that in other seasons. The prevalence of G. duodenalis in cattle in 2016 or later was 11.62% (95% CI: 6.49-20.79), which was significantly higher than that before 2016 (3.65%; 95% CI: 2.17-6.12). The highest prevalence of G. duodenalis in cattle was 74.23% (95% CI: 69.76-78.45) recorded in South China. The NOAA's National Center for Environmental Information (https://gis.ncdc.noaa.gov/maps/ncei/cdo/monthly) was used to extract relevant geoclimatic data (latitude, longitude, elevation, temperature, precipitation, humidity, and climate). By analyzing the data of each subgroup, it was shown that age of cattle, sampling year, province, region, temperature, and climate were potential risk factors for giardiasis prevalence in cattle. Based on the analysis of common factors and geographical factors, it is recommended to strengthen effective management measures (e.g., ventilation and disinfection in warm and humid areas) and formulate relevant policies according to local conditions. Breeders should pay more attention to the detection of G. duodenalis in calves, to prevent giardiasis prevalence in cattle of different ages, thereby reducing the economic losses of animal husbandry in China.

Immune subtype identification and multi-layer perceptron classifier construction for breast cancer.

Introduction: Breast cancer is a heterogeneous tumor. Tumor microenvironment (TME) has an important effect on the proliferation, metastasis, treatment, and prognosis of breast cancer. Methods: In this study, we calculated the relative proportion of tumor infiltrating immune cells (TIICs) in the breast cancer TME, and used the consensus clustering algorithm to cluster the breast cancer subtypes. We also developed a multi-layer perceptron (MLP) classifier based on a deep learning framework to detect breast cancer subtypes, which 70% of the breast cancer research cohort was used for the model training and 30% for validation. Results: By performing the K-means clustering algorithm, the research cohort was clustered into two subtypes. The Kaplan-Meier survival estimate analysis showed significant differences in the overall survival (OS) between the two identified subtypes. Estimating the difference in the relative proportion of TIICs showed that the two subtypes had significant differences in multiple immune cells, such as CD8, CD4, and regulatory T cells. Further, the expression level of immune checkpoint molecules (PDL1, CTLA4, LAG3, TIGIT, CD27, IDO1, ICOS) and tumor mutational burden (TMB) also showed significant differences between the two subtypes, indicating the clinical value of the two subtypes. Finally, we identified a 38-gene signature and developed a multilayer perceptron (MLP) classifier that combined multi-gene signature to identify breast cancer subtypes. The results showed that the classifier had an accuracy rate of 93.56% and can be robustly used for the breast cancer subtype diagnosis. Conclusion: Identification of breast cancer subtypes based on the immune signature in the tumor microenvironment can assist clinicians to effectively and accurately assess the progression of breast cancer and formulate different treatment strategies for different subtypes.