Ping Wang

With the increasing maturity of percutaneous transhepatic cholangioscopy and biliary drainage techniques, PTOBF has gradually emerged as a novel minimally invasive treatment for biliary strictures and hepatolithiasis. This retrospective single‑center study included 25 post‑liver transplant patients diagnosed with biliary strictures and stones between January 2022 and December 2025, aiming to further evaluate the clinical efficacy and safety of PTOBF. The degree of stricture, distribution of stones, total bilirubin (TBIL), direct bilirubin (DBIL), alanine aminotransferase (ALT), aspartate aminotransferase (AST), fibrinogen (FIB), γ-glutamyl transpeptidase (r-GGT), prothrombin time (PT), C-reactive protein(CRP), postoperative complication rate, stricture resolution rate, and stone clearance rate, etc., were analyzed. A total of 73 PTOBF procedures were performed on the 25 patients to manage strictures. The mean bile duct diameter after dilatation was 5.472 ± 0.667 mm. The stricture resolution rate was 92.0% (23/25). During a mean follow-up period of 19.1 ± 8.2 months, the final stone clearance rate was 88.0% (22/25). Compared with preoperative levels, postoperative levels of DBIL, ALT, AST, and FIB were significantly reduced. In this small retrospective cohort, PTOBF was associated with favorable stricture resolution and stone clearance rates, suggesting significant clinical potential. Prospective studies are needed to confirm efficacy and safety.

Accurate and prompt tumor diagnosis plays a critical role in surgical decision-making to minimize damage to patients with benign tumors and maximize the cure rate of cancer patients. Although imaging, molecular testing, and histopathology assist in preoperative and postoperative tumor assessments, a fast, automated, and easy-to-operate method for intraoperative tumor diagnosis is lacking. Here, we introduce an innovative integrated circuit-based single-cell electric cell-substrate impedance sensing (IC-ECIS) platform capable of rapid intraoperative differentiation between cancerous and non-cancerous tumors. Fabricated on a semiconductor chip, this system captures ultra-weak single-cell impedance signals with exceptional sensitivity for single-cell discrimination for cancer risk evaluation. The implementation of a polymer-embedded silicon fan-out (P-eSiFO) packaging approach has reduced post-fabrication costs to under $1 per chip and allowed the microelectrodes batch processing on multi-project wafer chips. The diagnostic results can be obtained in 20 min without compromising cell viability. Clinical tumor samples from the thyroid, liver, gallbladder, bile duct, breast, and pancreas were tested on-chip to evaluate cancer risk. The on-chip diagnostic findings aligned with traditional histopathological analysis and flow cytometry results. This technology provides a fast, stable, automated, and low-sample-consumption diagnostic solution for tumors, paving the way for large-scale implementation of semiconductor-based diagnostic tools in precision oncology and personalized medicine.

Pancreatic ductal adenocarcinoma (PDAC) remains a formidable challenge due to its late diagnosis and intrinsic treatment resistance, exacerbates by its development from chronic inflammation to cancer transition (ICT). Here, this investigation aims to develop and evaluate ABSi‐148, a novel near‐infrared (NIR) agent targeting hypoxic carbonic anhydrase IX (CA IX), for its potential applications in ICT imaging and even PDAC treatment. ABSi‐148 is synthesized from 4‐(2‐Aminoethyl) benzene sulfonamide (ABS), a sulfonamide derivative, conjugating with MHI‐148 dye with merits of exceptional NIR‐emitting traits, high biocompatibility, and deep tissue penetration imaging capability. It selectively accumulates in CoCl₂‐induced pancreatic stellate cells and pancreatic cancer cells via binding with transmembrane CA IX in vitro. Meanwhile, ABSi‐148 effectively visualizes the early pancreatic lesion, and its long‐term administration inhibits the progression of hypoxia‐related fibrosis involved in pancreatic intraepithelial neoplasias (PanINs), and even PDAC progression in vivo. Besides, ABSi‐148 monitors treatment efficacy and localizes hypoxic tumor regions, enhancing survival in tamoxifen combined with caerulein‐induced KPC mice. Overall, ABSi‐148 emerges as a theranostic NIR agent for precise diagnosis and targeted therapy in ICT of PDAC, promising to alleviate tumor progression and enhancing outcomes.

To aid the diagnosis of chronic obstructive pulmonary disease (COPD), a local-to-global deep framework with group attentions and slice-aware loss is designed in this paper, which utilizes the chest CT sequences of the patients as the network input. To fully mine the medical hints submerged in the CT slices, two types of group attentions are designed to extract local–global features of the grouped slices. Specifically, in each group, a group local attention block (GLAB) and a group global attention block (GGAB) are designed to extract local features in the CT slices and long-range dependencies among the grouped slices. To alleviate the influence of different numbers of CT slices in the chest CT sequences for different patients, a slice-aware loss is proposed by incorporating a normalized coefficient into the cross-entropy loss. Experimental results indicate that the designed deep model performs a good COPD identification on a real COPD dataset with 96.08% accuracy, 94.12% sensitivity, 97.06% specificity, and 95.32% AUC, which is superior to some existing deep learning methods.

Image segmentation is a key step of the 3D reconstruction of the hepatobiliary duct tree, which is significant for preoperative planning. In this paper, a novel 3D U-Net variant is designed for CT image segmentation of hepatobiliary ducts from the abdominal CT scans, which is composed of a 3D encoder-decoder and a 3D multi-feedforward self-attention module (MFSAM). To well sufficient semantic and spatial features with high inference speed, the 3D ConvNeXt block is designed as the 3D extension of the 2D ConvNeXt. To improve the ability of semantic feature extraction, the MFSAM is designed to transfer the semantic and spatial features at different scales from the encoder to the decoder. Also, to balance the losses for the voxels and the edges of the hepatobiliary ducts, a boundary-aware overlap cross-entropy loss is proposed by combining the cross-entropy loss, the Dice loss, and the boundary loss. Experimental results indicate that the proposed method is superior to some existing deep networks as well as the radiologist without rich experience in terms of CT segmentation of hepatobiliary ducts, with a segmentation performance of 76.54% Dice and 6.56 HD. Graphical Abstract

BACKGROUND Biliary stone disease is a highly prevalent condition and a leading cause of hospitalization worldwide. Hepatolithiasis with associated strictures has high residual and recurrence rates after traditional multisession percutaneous transhepatic cholangioscopic lithotripsy (PTCSL). AIM To study one-step PTCSL using the percutaneous transhepatic one-step biliary fistulation (PTOBF) technique guided by three-dimensional (3D) visualization. METHODS This was a retrospective, single-center study analyzing, 140 patients who, between October 2016 and October 2023, underwent one-step PTCSL for hepatolithiasis. The patients were divided into two groups: The 3D-PTOBF group and the PTOBF group. Stone clearance on choledochoscopy, complications, and long-term clearance and recurrence rates were assessed. RESULTS Age, total bilirubin, direct bilirubin, Child-Pugh class, and stone location were similar between the 2 groups, but there was a significant difference in bile duct strictures, with biliary strictures more common in the 3D-PTOBF group (P = 0.001). The median follow-up time was 55.0 (55.0, 512.0) days. The immediate stone clearance ratio (88.6% vs 27.1%, P = 0.000) and stricture resolution ratio (97.1% vs 78.6%, P = 0.001) in the 3D-PTOBF group were significantly greater than those in the PTOBF group. Postoperative complication (8.6% vs 41.4%, P = 0.000) and stone recurrence rates (7.1% vs 38.6%, P = 0.000) were significantly lower in the 3D-PTOBF group. CONCLUSION Three-dimensional visualization helps make one-step PTCSL a safe, effective, and promising treatment for patients with complicated primary hepatolithiasis. The perioperative and long-term outcomes are satisfactory for patients with complicated primary hepatolithiasis. This minimally invasive method has the potential to be used as a substitute for hepatobiliary surgery.

Bilestones are solid masses found in the gallbladder or biliary tract, which block the normal bile flow and eventually result in severe life-threatening complications. Studies have shown that bilestone formation may be related to bile flow dynamics and the concentration level of bile components. The bile flow dynamics in the biliary tract play a critical role in disclosing the mechanism of bile stasis and transportation. The concentration of bile composition is closely associated with processes such as nucleation and crystallization. Recently, microfluidic-based biosensors have been favored for multiple advantages over traditional benchtop detection assays for their less sample consumption, portability, low cost, and high sensitivity for real-time detection. Here, we reviewed the developments in bile dynamics study and microfluidics-based bile component detection methods. These studies may provide valuable insights into the bilestone formation mechanisms and better treatment, alongside our opinions on the future development of in vitro lithotriptic drug screening of bilestones and bile characterization tests.

Accurate segmentation of hepatic vessel is significant for the surgeons to design the preoperative planning of liver surgery. In this paper, a sequence-based context-aware association network (SCAN) is designed for hepatic vessel segmentation, in which three schemes are incorporated to simultaneously extract the 2D features of hepatic vessels and capture the correlations between adjacent CT slices. The two schemes of slice-level attention module and graph association module are designed to bridge feature gaps between the encoder and the decoder in the low- and high-dimensional spaces. The region-edge constrained loss is designed to well optimize the proposed SCAN, which integrates cross-entropy loss, dice loss, and edge-constrained loss. Experimental results indicate that the proposed SCAN is superior to several existing deep learning frameworks, in terms of 0.845 DSC, 0.856 precision, 0.866 sensitivity, and 0.861 F1-score. Graphical Abstract