With the development of urbanization and industrialization， increasingly severe air pollution and frequent extreme weather events pose a significant threat to public health. The objective of this study was to evaluate the effects of meteorological factors and air pollution on respiratory deaths. The study dataset includes meteorological， air pollutant， and respiratory disease death data from Haidian District， Beijing， China， from January 1， 2014 to July 31， 2024. A random forest （RF） model was used to analyze the effects of meteorological factors and air pollutant levels on respiratory disease mortality， and the factors influencing respiratory disease mortality were analyzed in combination with SHapley Additive exPlanations （SHAP）. The results of Spearman correlation analysis and the RF model showed that SO₂， NO₂， PM_2.5 and PM₁₀ concentrations were positively correlated with respiratory mortality， whereas the minimum temperature was negatively correlated with respiratory mortality. In addition， the model showed better prediction performance in winter than in other seasons. Furthermore， the SHAP global feature results indicate that the minimum temperature is the most significant factor affecting mortality from respiratory diseases.The results show that the RF model has the potential to predict deaths from respiratory diseases， since it can effectively combine meteorological and air pollution data. Combined with SHAP， it can further enhance the interpretability of the machine learning model. This study should provide strong support for policymakers in scientifically formulating targeted air quality control measures， health warnings about extreme temperatures， and prevention and control strategies for seasonal respiratory diseases.

Faced with multiple diseases such as chronic obstructive pulmonary disease （COPD）， cardiogenic pulmonary edema and obstructive sleep apnea-hypopnea syndrome （OSAHS）， there is an urgent need for non-invasive ventilation （NIV） masks. Traditional NIV masks have problems such as air leakage， limited adaptability and secondary contusion for patients. A personalized NIV mask with shape memory characteristics based on Eucommia ulmoides gum （EUG） has been developed in this study. Styrene-ethylene-butene-styrene block copolymer （SEBS） was filled into Eucommia ulmoides gum by physical blending to accelerate its shape fixation rate， and a series of hard EUG-SEBS shape memory materials were prepared. Meanwhile， natural rubber （NR） was added to Eucommia ulmoides gum to prepare a series of soft EUG-NR shape memory materials. Adding NR gradually reduces the hardness gradient of the composite material while ensuring a high shape-fixation rate. The experimental results show that the shape-fixation rate of EUG-5SEBS had the highest value of 95.5%； when the mass ratio of EUG to NR was 80/20， the composite material exhibited the best comprehensive performance， with a shape-fixation rate of 75.78% and a Shore A hardness of 87. Layers of rigid polyurethane， EUG-5SEBS， EUG80-NR20 and NR， were combined to manufacture personalized non-invasive ventilation masks with shape-memory properties to meet the goals of compliance across different face shapes. A comfort survey showed that this mask was more comfortable than conventional airbag masks.

Coastal cities in southern China have a humid coastal climate， providing ideal natural conditions for the reproduction of microorganisms. Understanding the distribution of microbial communities and associated resistance genes in the relevant areas of coastal cities is crucial for assessing the potential transmission risks of diseases and infections. Taking the green spaces of a coastal city in Hainan Province as a representative example， microbial identification and resistance gene analysis were carried out in nearby public facilities. The collected microbial samples were first isolated， cultured and purified， and species identification was completed using 16S rRNA and ITS amplicon sequencing analysis. Subsequently， the potential resistance genes of these microorganisms were analyzed using metagenomics. The results show that the main microorganisms in this area include Bacillus sp.， Providencia sp.， Proteus sp.， Alternaria sp.， Fusarium sp. and Aspergillus sp.. Genes related to metabolism are relatively abundant in these microorganisms， especially those associated with carbohydrate and amino acid metabolism. Antibiotic resistance genes vanY， vanW， vanT and FosBx1 were detected in these microorganisms. These resistance genes are associated with vancomycin and fosfomycin resistance， suggesting a potential risk of antibiotic resistance in this area.

Molecular diagnostic technology plays an important role in pathogenic microorganism detection， epidemic prevention and control， disease diagnosis and precision medicine， but it has the disadvantages of long turnaround times， low sensitivity and poor specificity. Therefore， there is an urgent need to develop rapid， sensitive and specific molecular diagnostic techniques. In this study， a single-molecule detection method without amplification was developed by combining the CRISPR/Cas13 system and total internal reflection fluorescence microscopy （TIRF）. The conserved 2×HEPN domain of Cas13 protein mutated into dCas13 （deactivated Cas13）， resulting in the loss of nuclease activity but retaining the activity of the conjugating enzyme， allowing the dCas13 protein to specifically recognize and bind RNA molecules. Then， the trisomy complex formed by the dCas13 protein， the sgRNA （fluorescent group labeled） and the S gene （target RNA） of the SARS-CoV-2 virus in the reaction system was captured using the capture probe， and the target RNA molecules were detected by TIRF. The experimental results show that， under the condition of no target amplification， the established single-molecule detection method had a detection sensitivity of 1 pmol/L for the target RNA. Compared with the inherent accessory cleavage activity of the CcaCas13b protein， the sensitivity shows a 1 000-fold increase. This detection system has high specificity and can effectively distinguish the S gene of the SARS-CoV-2 virus and its common mutants （N501Y and D614G）. In addition， the genomic RNA of the SARS-CoV-2 virus （158 ng/µL） was successfully detected using this method. The single-molecule detection technology established in this study affords high sensitivity and strong specificity， and does not require additional nucleic acid amplification steps. Our work provides new ideas for the development of subsequent rapid diagnostic methods and has potential application value.

Super⁃resolution reconstruction of ultrasound images can effectively improve image quality by enhancing the high⁃frequency information and enriching the detailed features of the images. To address the problem that existing super⁃resolution reconstruction of ultrasound images is prone to detail distortion in this work， a method for super⁃resolution reconstruction of lung ultrasound images based on texture feature enhanced generative adversarial networks （TFEGAN） is proposed. This method employs a multi⁃scale texture feature extraction module that fully extracts texture information at multiple scales form lung ultrasound images using a multi⁃branch architecutre. It employs channel attention mechanisms and multi⁃head self⁃attention mechanisms to enhance the feature extraction of the super-resolution generative adversarial network. As a result the network is able to dynamically adjust the weights of different channel features and capture long-range dependencies， thereby enhancing its global feature representation capabilities. Finally， by combining a joint loss function with an adaptive loss-weight adjustment strategy， super-resolution reconstruction of lung ultrasound images was achieved. Experimental results show that compared to conventional algorithms such as super-resolution generative adversarial network （SRGAN）， enhanced super-resolution generative adversarial networks （ESRGAN）， structure-preserving super resolution （SPSR） and content-aware local GAN （CAL-GAN）， the learned perceptual image patch similarity （LPIPS） index of our new method is improved by 17.3%， 3.76%， 9.70%， and 2.85%. The reconstructed image texture details are discernible， and the overall image quality is enhanced.

Diabetic retinopathy （DR）， one of the common chronic complications of diabetes， is of great significance in clinical practice as its accurate classification helps ophthalmologists tailor treatment plans for patients. Diabetic macular edema （DME）， a complication closely related to DR， is often used for multi-task learning with DR to assist in DR diagnosis. Currently， deep learning methods for DR grading diagnosis mainly focus on network architecture design， while research on data augmentation techniques is relatively limited. This paper proposes a novel data augmentation method， GreenBen， that combines feature redundancy reduction of the green channel with the background suppression capability of Ben enhancement. Despite its simple design， this method is highly effective. Extensive experiments conducted on three public datasets show that GreenBen achieves stable and significant performance improvements compared with other data augmentation methods， with an average accuracy improvement of 4% and a maximum improvement of up to 10%， regardless of whether it is used for single DR classification or multi-task joint classification， or with CNN or Transformer models.

In order to provide a method for the rapid preparation of high-performance fiber materials to meet the medical needs of the army in field emergencies and personal protection， in this work， a portable melt differential electrostatic spinning machine product has been designed. Using the Teoriya Resheniya Izobreatatelskikh Zadatch （TRIZ） theory， we analyzed the traditional process for designing a portable melt differential electrostatic spinning machine and optimized the melt conversion， electric field generation and control system using the invention principles of splitting， extracting and nesting. The resulting innovative portable melt differential electrostatic spinning machine can effectively address the many limitations of the traditional spinning machines in different military missions arising from their poor portability， complex operation， insufficient adaptability to the environment and other problems. Our new design is expected to enhance the emergency protection capability and combat effectiveness of the military， provide innovative solutions for the military medical field， and serve as a theoretical reference for further innovation in electrostatic spinning technology and related equipment.

This study investigated the effects of varying temperature， voltage， air pressure， collection speed， collection height， and nozzle inner diameter on scaffold thickness using poly（ε-caprolactone） （PCL） through single-factor experiments. A fiber layer packing index （F _p） was defined to quantify the tightness of the fiber arrangement or interlayer bonding. Experimental results revealed that： elevated temperature increased fiber diameter and intensified interlayer fusion， leading to significant scaffold height reduction； Higher voltage refined the fibers but caused disordered deposition and a sharp reduction in layer number at excessive levels； Increased air pressure thickened the fibers and induced interlayer fusion； Faster collection speed refined the fibers while reducing thickness； Greater collection height significantly increased layer number but reduced interlayer adhesion when excessively high； Smaller nozzle inner diameter refined the fibers but caused a disordered fiber arrangement below critical dimensions. The maximum scaffold thickness reached 3.43 mm with 235 layers. Additionally， three failure modes were observed： disordered fiber deposition， polymer droplet formation， and interlayer fusion.

Progesterone fiber patches were fabricated using melt electrospinning technology， with a focus on screening permeation enhancers for progesterone and investigating the effects of process parameters （spinning temperature， voltage， and air pressure） on fiber morphology and fineness. In vitro transdermal experiments and air permeability tests were conducted， and comparisons were made with traditional coated patches. The results demonstrated that isopropyl myristate exhibited a significant permeation-enhancing effect on progesterone. The optimal parameters were determined as follows： spinning temperature of 140 °C， spinning air pressure of 0.5 psi （1 psi=6 894.757 Pa） and voltage of 6.0 kV， yielding fibers with an average diameter of （47.08±12.43） μm. The optimized fiber patches outperformed conventional coated patches in terms of drug release rate， transdermal efficiency， and breathability. Notably， when using porcine skin as the penetration barrier， the daily permeation flux of progesterone （30.315 μg/cm²） approached the common clinical administration requirement （35.710 μg/cm²）. Furthermore， the cumulative permeation amount through murine skin reached 1.45 times that of coated patches. This study provides an effective strategy for developing novel progesterone-loaded melt-electrospun fiber patches.

Computed tomography （CT） is currently the most common diagnostic method for brain hemorrhages. Rapidly identifying the location and shape of a brain hemorrhage using deep learning models is of great clinical significance for locating its area and determining its cause. However， most of the mainstream medical segmentation models encounter under-segmentation problems during the segmentation of brain hemorrhages， especially in the region near the skull or when the hemorrhage volume is small. For this reason， this paper proposes a brain hemorrhage segmentation method based on multimodal text representation， which uses a contrastive language-image pre-training （CLIP） model to encode the designed prompts for representation. The text encoder in CLIP can represent information in prompts about the relative location， inclusion relation and other parameters. The representation can then be combined with the U-net to perform the brain hemorrhage segmentation task. The method proposed in this paper uses flexible text prompts to address the problem that some parts of a brain hemorrhage are difficult to segment， thereby enhancing the precision of segmentation. The medical segmentation performance metrics （Dice coefficient） of our method reached 43.3% and 58.8% respectively， when using the publicly available Brain Hemorrhage Segmentation Dataset （BHSD） and the brain hemorrhage dataset for an individual hospital. The improved performance of our method compared with other single medical segmentation models provides strong evidence of its effectiveness.

Acute appendicitis （AA） CT diagnosis has long faced the clinical challenge of low efficiency and high misdiagnosis rates in primary healthcare settings. To address this problem， we have developed an intelligent diagnostic system based on an improved nnU⁃Net， incorporating three key innovations： （1） a dynamically weighted composite loss function that combines Dice and cross⁃entropy losses and adjusts weights according to the training process， effectively improving segmentation accuracy for small appendiceal targets； （2） an edge⁃enhanced supervision mechanism that strengthens the model’s perception of appendiceal boundaries through edge information； and （3） the use of Shapley Additive exPlanations （SHAP） to quantify the impact of key morphological features on diagnostic decisions， thereby enhancing system interpretability. We trained the system on CT data from 60 clinically confirmed acute appendicitis patients and evaluated it on an independent test set of 30 cases （15 with appendicitis， 15 normal）. The system achieved a Dice coefficient of 72.4% in appendiceal segmentation. In terms of diagnostic performance， the AI system （sensitivity 73.3%， specificity 80.0%， accuracy 76.7%） performed comparably to senior physicians. Moreover， the system achieved an average diagnostic time of only 23.5 seconds， significantly improving efficiency. These findings suggest that our new AI system offers accuracy， speed， and interpretability， and has broad clinical application potential.

Segmentation of lumbar vertebrae in CT images has significant importance in the auxiliary diagnosis and treatment of lumbar diseases. The U-Net architecture and its extended models have attracted extensive attention in the field of medical image segmentation. By addressing issues such as the loss of fine-grained features in lumbar segmentation using the Rolling U-Net model， an improved Rolling U-Net model is proposed for segmenting lumbar CT images. This model integrates a convolutional neural network （CNN） with a multi-layer perceptron （MLP）. Feature excitation modules are inserted at the fourth convolutional layer and the bottleneck layer to increase the weight of key anatomical structures. By constructing long-range-local blocks （Lo2 blocks）， it achieves the fusion of local feature information with long-range dependencies. The core R-MLP module within the Lo2 block learns long-range dependencies across the entire image in a single direction. By controlling and combining R-MLP modules oriented in different directions， OR-MLP and DOR-MLP modules are constructed to capture long-range dependencies in multiple directions. Finally， residual convolutions are integrated to restore segmentation details in the lumbar spine. Simultaneously， a MultiClassDiceCE loss function is designed by combining the pixel classification advantages of the Dice loss function and the cross-entropy loss function. Experimental results indicate that the number of categories and sampling strategies significantly impact the segmentation performance of the improved Rolling U-Net model. For binary segmentation tasks， the division strategy based on the total number of images is recommended to balance accuracy and stability， whereas the division strategy based on the total number of instances is better suited for multi-classification tasks. When performing multi-class segmentation tasks on the JST_LV and VerSe datasets， the improved Rolling U-Net model outperformed segmentation models such as U-Net， Attention U-Net， and Rolling U-Net in terms of average Intersection over Union （IoU）， Dice coefficient， recall， specificity， and precision. This demonstrates that the improved model effectively enhances the accuracy， integrity of detail， and classification robustness of lumbar spine CT image segmentation.

To address the problem of noise degrading fusion performance in medical images， we propose a multimodal medical image fusion method based on cross⁃modal channel-aware （CMCA） module. A dual-branch encoder enhanced with a squeeze-and-excitation module is introduced to apply channel-wise weighting during feature extraction and obtain modality-specific features. A cross-modal channel-aware fusion module is constructed to integrate the extracted information and achieve complementary feature fusion. A composite loss function combining image entropy and median-based weighting is adopted to preserve detail while suppressing noise during training， thereby enabling effective multimodal medical image fusion. Experimental results on an MRI⁃CT dataset show that the proposed method achieves an average gradient of 8.630， a standard deviation of 82.301 with a spatial frequency of 35.728， and a structural similarity index of 1.173， which makes it a valuable tool for assisting clinicians in lesion analysis.