Issue Information

Association of age at pancreatic cancer diagnosis with smoking and drinking: A hospital‐based and meta‐analysis study

Abstract Recent trends show an alarming rise in pancreatic cancer prevalence among young individuals. Previous studies have presented differences in age at diagnosis. The study aimed to examine the likelihood of a younger age at diagnosis in individuals exposed to known risk factors, compared with those without exposure. We conducted a retrospective cohort analysis of hospitalized patients in Taiwan. The Kaplan–Meier method and a Cox proportional hazards model were used to evaluate survival probabilities and diagnosis risk, respectively. We then performed a systematic review and meta‐analysis to examine the association of the risk of early‐diagnosis pancreatic cancer with smoking and alcohol drinking across different countries. In the cohort of 121 patients with pancreatic cancer, smokers and drinkers were diagnosed at 5.8 and 5.9 years younger than non‐smokers and non‐drinkers, respectively, while those who both smoked and drank were diagnosed 9.7 years earlier. Survival analysis confirmed these findings after adjusting for covariates (smokers: hazard ratio [HR] = 1.75, p  = .019; drinkers: HR = 1.78, p  = .023), with patients who both smoked and drank having a substantially higher risk of earlier diagnosis (HR = 3.66, 95% confidence interval = 1.97–6.81, p  < .001). Meta‐analysis revealed that early smoking initiation (<20 years) was linked to a higher risk of pancreatic cancer (pooled HR = 1.46) compared with late initiation (≥20 years) (pooled HR = 1.39), consistent across various stratifications. Cigarette smoking and alcohol drinking are linked to younger age at pancreatic cancer diagnosis, with early smoking initiation increasing this risk further. These findings underscore the need for targeted interventions to reduce smoking and excessive alcohol drinking to delay the onset and reduce the incidence of pancreatic cancer, highlighting the importance of early preventive measures.

Achalasia after laparoscopic sleeve gastrectomy treated with peroral endoscopic myotomy

Pyoderma gangrenosum in ulcerative colitis

Pancreatic acinar cell carcinoma with multiple liver metastases presented with pancreatic panniculitis

A male with postprandial abdominal pain

Amelanotic melanoma metastatic to the duodenum

The hidden fragility: Unmasking the systematic link between lean metabolic dysfunction‐associated fatty liver disease and osteoporosis

A CRDNet‐Based Watermarking Algorithm for Fused Visible–Infrared Images

Infrared images often involve trade secrets. In order to protect the information security of infrared images, using a robust watermarking algorithm based on infrared images is a fitting solution. Recently, many effective visible and infrared image fusion (VIF) algorithms have been proposed in medicine, biology, and geology. Robust watermarking algorithms can resist mild conventional attacks, and with the progress of science and technology, algorithmic robustness against novel attacks such as screen shooting and print shooting has also become a research hotspot. However, VIF‐based image watermarking algorithms are still scarce. It is important to investigate a robust watermarking algorithm that can resist VIF attacks. Herein, an autoencoder against infrared fusion attacks is proposed based on various subnetworks: CRDnet (Convolutional Residual Dense Network). CRDnet includes encoders and decoders based on residual and dense structures, a fusion network robust to 12 VIF algorithms, and predictors for predicting watermarked infrared images. The encoder and decoder also incorporate preprocessing steps, attention mechanisms, and activation functions suitable for infrared images. The experimental results demonstrate that the bit error rate of CRDnet is reduced by at least ≈4% compared to common autoencoders. The peak signal‐to‐noise ratio of watermarked images is also almost always greater than 38 dB.

Disentangling Coincident Cell Events Using Deep Transfer Learning and Compressive Sensing

Accurate single‐cell analysis is critical for diagnostics, immunomonitoring, and cell therapy, but coincident events, where multiple cells overlap in a sensing zone, can severely compromise signal fidelity. A hybrid framework combining a fully convolutional neural network (FCN) with compressive sensing (CS) to disentangle such overlapping events in 1D sensor data is presented. The FCN, trained on bead‐derived datasets, accurately estimates coincident event counts and generalizes to immunomagnetically labeled CD4 + and CD14 + cells in whole blood without retraining. Using this count, the CS module reconstructs individual signal components with high fidelity, enabling precise recovery of single‐cell features, including velocity, amplitude, and hydrodynamic diameter. Benchmarking against conventional state‐machine algorithms shows superior performance, recovering up to 21% more events and improving classification accuracy beyond 97%. Explainability via class activation maps and parameterized Gaussian template fitting ensures transparency and clinical interpretability. Demonstrated with magnetic flow cytometry (MFC), the framework is compatible with other waveform‐generating modalities, including impedance cytometry, nanopore, and resistive pulse sensing. This work lays the foundation for next‐generation nonoptical single‐cell sensing platforms that are automated, generalizable, and capable of resolving overlapping events, broadening the utility of cytometry in translational medicine and precision diagnostics, e.g., cell‐interaction studies.