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To address the challenges associated with preparing thin strips of difficult-to-deform materials – such as low processing efficiency and yield, poor shape, and subpar performance – our research team developed an on-line warm rolling experimental machine featuring hydraulic tension. We used the Ti–6Al–4V alloy as our experimental material and conducted warm rolling experiments at various temperatures, thoroughly examining the intricate relationship between microstructure evolution and mechanical properties. Our findings indicate that warm rolling causes a clear refinement of the α phase and β phase microstructures, while intensifying dislocation entanglement. These changes significantly enhance the strength of the Ti–6Al–4V alloy, though Total Elongation Limit (TEL) decreases. After evaluating mechanical properties, surface quality, and energy consumption associated with warm rolling, we determined the optimal warm rolling temperature for the Ti–6Al–4V alloy to be 650 °C. At this temperature, the Ultimate Tensile Strength (UTS), Yield Strength (YS), and TEL were 1325 MPa, 1164 MPa, and 8.0%, respectively, maintaining good ductility. The impressive performance of the Ti–6Al–4V alloy sheet is mainly attributed to the strengthening effects of fine grain and dislocation, while the introduction of some fine recrystallization enhances ductility. High-performance Ti–6Al–4V alloy thin strip was successfully prepared by on-line warm rolling with hydraulic tension, which provided a new idea for the preparation of thin strip of difficult-to-deform materials.

Abstract The landscape of current cancer immunotherapy is dominated by antibodies targeting PD-1/PD-L1 and CTLA-4 that have transformed cancer therapy, yet their efficacy is limited by primary and acquired resistance. The blockade of additional immune checkpoints, especially TIGIT and LAG-3, has been extensively explored, but so far only a LAG-3 antibody has been approved for combination with nivolumab to treat unresectable or metastatic melanoma. Here we report the development of a PDL1 × TIGIT bi-specific antibody (bsAb) GB265, a PDL1 × LAG3 bsAb GB266, and a PDL1 × TIGIT × LAG3 tri-specific antibody (tsAb) GB266T, all with intact Fc function. In in vitro cell-based assays, these antibodies promote greater T cell expansion and tumor cell killing than benchmark antibodies and antibody combinations in an Fc-dependent manner, likely by facilitating T cell interactions (bridging) with cancer cells and monocytes, in addition to blocking immune checkpoints. In animal models, GB265 and GB266T antibodies outperformed benchmarks in tumor suppression. This study demonstrates the potential of a new generation of multispecific checkpoint inhibitors to overcome resistance to current monospecific checkpoint antibodies or their combinations for the treatment of human cancers.

Many micro-scraps are generated when a micro-triangular pyramid array (MTPA) is machined by the fly-cutting method. Micro-scraps are generally not removed quickly enough; therefore, these residual micro-scraps participate in the cutting process again, scratching the workpiece surface and accelerating diamond tool wear. To remove micro-scraps rapidly, a fly-cutting method to produce MTPAs on vertically oriented working surfaces was developed during this study. The results show that an MTPA produced by fly cutting on a vertical workpiece had a clearly outlined structure, high dimensional accuracy, and a low surface roughness. There was no micro-scrap residue on the workpiece surface and the diamond tool wear was small. The cutting inlet edges had no burrs, and the cutting outlet edges had only a small number of burrs. This method of fly cutting MTPAs on vertically oriented working surfaces provides a foundation for the development of high-precision micro-triangular pyramid optical elements.

ObjectivesAnterior mediastinal disease is a common disease in the chest. Computed tomography (CT), as an important imaging technology, is widely used in the diagnosis of mediastinal diseases. Doctors find it difficult to distinguish lesions in CT images because of image artifact, intensity inhomogeneity, and their similarity with other tissues. Direct segmentation of lesions can provide doctors a method to better subtract the features of the lesions, thereby improving the accuracy of diagnosis.MethodAs the trend of image processing technology, deep learning is more accurate in image segmentation than traditional methods. We employ a two-stage 3D ResUNet network combined with lung segmentation to segment CT images. Given that the mediastinum is between the two lungs, the original image is clipped through the lung mask to remove some noises that may affect the segmentation of the lesion. To capture the feature of the lesions, we design a two-stage network structure. In the first stage, the features of the lesion are learned from the low-resolution downsampled image, and the segmentation results under a rough scale are obtained. The results are concatenated with the original image and encoded into the second stage to capture more accurate segmentation information from the image. In addition, attention gates are introduced in the upsampling of the network, and these gates can focus on the lesion and play a role in filtering the features. The proposed method has achieved good results in the segmentation of the anterior mediastinal.ResultsThe proposed method was verified on 230 patients, and the anterior mediastinal lesions were well segmented. The average Dice coefficient reached 87.73%. Compared with the model without lung segmentation, the model with lung segmentation greatly improved the accuracy of lesion segmentation by approximately 9%. The addition of attention gates slightly improved the segmentation accuracy.ConclusionThe proposed automatic segmentation method has achieved good results in clinical data. In clinical application, automatic segmentation of lesions can assist doctors in the diagnosis of diseases and may facilitate the automated diagnosis of illnesses in the future.

(1) Background: Chest radiographs are the mainstay of initial radiological investigation in this COVID-19 pandemic. A reliable and readily deployable artificial intelligence (AI) algorithm that detects pneumonia in COVID-19 suspects can be useful for screening or triage in a hospital setting. This study has a few objectives: first, to develop a model that accurately detects pneumonia in COVID-19 suspects; second, to assess its performance in a real-world clinical setting; and third, by integrating the model with the daily clinical workflow, to measure its impact on report turn-around time. (2) Methods: The model was developed from the NIH Chest-14 open-source dataset and fine-tuned using an internal dataset comprising more than 4000 CXRs acquired in our institution. Input from two senior radiologists provided the reference standard. The model was integrated into daily clinical workflow, prioritising abnormal CXRs for expedited reporting. Area under the receiver operating characteristic curve (AUC), F1 score, sensitivity, and specificity were calculated to characterise diagnostic performance. The average time taken by radiologists in reporting the CXRs was compared against the mean baseline time taken prior to implementation of the AI model. (3) Results: 9431 unique CXRs were included in the datasets, of which 1232 were ground truth-labelled positive for pneumonia. On the “live” dataset, the model achieved an AUC of 0.95 (95% confidence interval (CI): 0.92, 0.96) corresponding to a specificity of 97% (95% CI: 0.97, 0.98) and sensitivity of 79% (95% CI: 0.72, 0.84). No statistically significant degradation of diagnostic performance was encountered during clinical deployment, and report turn-around time was reduced by 22%. (4) Conclusion: In real-world clinical deployment, our model expedites reporting of pneumonia in COVID-19 suspects while preserving diagnostic performance without significant model drift.

Tissue resident memory CD8 T cells (TRM) serve as potent local sentinels and contribute significantly to protective immunity against intracellular mucosal pathogens. While the molecular and transcriptional underpinnings of TRM differentiation are emerging, how TRM establishment is regulated by other leukocytes in vivo is largely unclear. Here, we observed that expression of PPAR-γ in the myeloid compartment was a negative regulator of CD8 TRM establishment following influenza virus infection. Interestingly, myeloid deficiency of PPAR-γ resulted in selective impairment of the tissue-resident alveolar macrophage (AM) compartment during primary influenza infection, suggesting that AM are likely negative regulators of CD8 TRM differentiation. Indeed, influenza-specific CD8 TRM cell numbers were increased following early, but not late ablation of AM using the CD169-DTR model. Importantly, these findings were specific to the parenchyma of infected tissue as circulating memory T cell frequencies in lung and TCM and TEM in spleen were largely unaltered following macrophage ablation. Further, the magnitude of the effector response could not explain these observations. These data indicate local regulation of pulmonary TRM differentiation is alveolar macrophage dependent. These, findings could aid in vaccine design aimed at increasing TRM density to enhance protective immunity, or deflating their numbers in conditions where they cause overt or veiled chronic pathologies.

Influenza virus causes a heterogeneous respiratory infectious disease ranging from self-limiting symptoms to non-resolving pathology in the lungs. Worldwide, seasonal influenza infections claim ~500,000 lives annually. Recent reports describe pathologic pulmonary sequelae that result in remodeling the architecture of lung parenchyma following respiratory infections. These dysfunctional recovery processes that disproportionately impact the elderly have been understudied. Macrophages are involved in tissue remodeling and are critical for survival of severe influenza infection. Here, we found intrinsic deficiency of the nuclear receptor PPAR-γ in myeloid cells delayed the resolution of pulmonary inflammation following influenza infection. Mice with myeloid cell-specific PPAR-γ deficiency subsequently presented with increased influenza-induced deposition of pulmonary collagen compared to control mice. This dysfunctional lung remodeling was progressive and sustained for at least 3 months following infection of mice with myeloid PPAR-γ deficiency. These progressive changes were accompanied by a pro-fibrotic gene signature from lung macrophages and preceded by deficiencies in activation of genes involved with damage repair. Importantly similar aberrant gene expression patterns were also found in a secondary analysis of a study where macrophages were isolated from patients with fibrotic interstitial lung disease. Quite unexpectedly, mice with PPAR-γ deficient macrophages were more resistant to bleomycin-induced weight loss whereas extracellular matrix deposition was unaffected compared to controls. Therefore PPAR-γ expression in macrophages may be a pathogen-specific limiter of organ recovery rather than a ubiquitous effector pathway in response to generic damage.

The purpose of this study was to explore the effects of direction of exertion (DOE) (pushing, pulling), path (walking in a straight line, turning left, walking uphill), and load placement (LP) (the 18 blocks were indicated by X, Y and Z axis; there were 3 levels on the X axis, 2 levels on the Y axis, and 3 levels on the Z axis) on muscle activity and ratings of perceived exertion in nursing cart pushing and pulling tasks. Ten participants who were female students and not experienced nurses were recruited to participate in the experiment. Each participant performed 108 experimental trials in the study, consisting of 2 directions of exertion (push and pull), 3 paths, and 18 load placements (indicated by X, Y and Z axes). A 23kg load was placed into one load placement. The dependent variables were electromyographic (EMG) data of four muscles collected bilaterally as follows: Left (L) and right (R) trapezius (TR), flexor digitorum superficialis (FDS), extensor digitorum (ED), and erector spinae (ES) and subjective ratings of perceived exertion (RPE). Split-split-plot ANOVA was conducted to analyze significant differences between DOE, path, and LP in the EMG and RPE data. Pulling cart tasks produced a significantly higher activation of the muscles (RTR:54.4%, LTR:50.3%, LFDS:57.0%, LED:63.4%, RES:40.7%, LES:36.7%) than pushing cart tasks (RTR:42.4%, LTR:35.1%, LFDS:32.3%, LED:55.1%, RES:33.3%, LES:32.1%). A significantly greater perceived exertion was found in pulling cart tasks than pushing cart tasks. Significantly higher activation of all muscles and perceived exertion were observed for walking uphill than walking in a straight line and turning left. Significantly lower muscle activity of all muscles and subject ratings were observed for the central position on the X axis, the bottom position on the Y axis, and the posterior position on the Z axis. These findings suggest that nursing staff should adopt forward pushing when moving a nursing cart, instead of backward pulling, and that uphill paths should be avoided in the design of work environments. In terms of distribution of the load in a nursing cart, heavier materials should be positioned at bottom of the cabinet, centered on the horizontal plane and close to the handle, to reduce the physical load of the nursing staff.