ObjectiveThe widespread adoption of portable fundus cameras for primary care and community screening is hindered by limitations in current autofocus(AF) technologies. Image-based methods relying on sharpness evaluation require iterative searches, resulting in slow convergence, while projection-based techniques are susceptible to optical artifacts and calibration errors. To address these challenges, this study introduces a novel AF system based on direct wavefront sensing, designed to deliver simultaneous high speed, high precision, and operational robustness within the compact form factor essential for portable ophthalmic devices. MethodsOur approach fundamentally reimagines the AF process by directly measuring the ocular wavefront aberration. We developed a custom portable fundus camera integrating a miniaturized Shack-Hartmann wavefront sensor (SHWS) into the optical path. An 850 nm laser diode projects a point source onto the retina via oblique illumination to minimize corneal reflections. Light scattered from this spot carries the eye’s refractive error through the imaging optics and is directed to the SHWS, positioned at a plane optically conjugate to the primary color CMOS imaging sensor. A microlens array within the SHWS samples the incident wavefront, generating a pattern of focal spots on a CCD. Real-time centroid analysis of these spots provides a map of local wavefront slopes. These measurements are processed through a singular value decomposition (SVD) algorithm to fit a Zernike polynomial basis set, enabling real-time reconstruction of the wavefront phase. The defocus component (S) is extracted from the second-order Zernike coefficients, providing a direct, quantitative measure of the refractive error in diopters. This value serves as a precise error signal in a closed-loop control system, which commands a voice-coil actuated focusing lens to its null position in a single, deterministic step, eliminating the need for iterative search algorithms. ResultsComprehensive evaluation demonstrated the system’s high performance. Testing on a calibrated model eye (OEMI-7) established a highly linear relationship between the computed defocus S and the focusing lens position across a ±20 Diopter (D) compensation range, achievable within a 5 mm mechanical travel. The system achieved a focusing precision of 0.08 D, corresponding to an 18-fold improvement over a conventional projection spot-size method tested under identical conditions. The total focus acquisition time, encompassing wavefront measurement, computation, and lens actuation, averaged under 0.5 s. Clinical validation with 25 human volunteers (50 eyes, refractive range -15 D to +10 D) confirmed practical efficacy. The wavefront-sensing AF succeeded in 92% of attempts with a mean time of 0.5 s, substantially outperforming a projection-based benchmark which achieved only a 32% success rate with an average time of 4.25 s. The system provided instantaneous directional guidance and maintained stability during minor ocular movements. Objective assessment of image quality, via amplitude contrast of retinal vasculature, showed consistent and significant enhancement following AF correction across the entire tested diopter range. ConclusionThis work successfully implements and validates a direct wavefront-sensing autofocus paradigm for portable fundus cameras. By directly quantifying and compensating for the optical defocus aberration, this method bypasses the fundamental limitations of image-processing and projection-based techniques, enabling rapid, precise, and deterministic diopter compensation. The developed system delivers an exceptional combination of a wide operational range (±20 D), high accuracy (0.08 D), fast convergence (0.5 s), and a compact physical footprint. This technology provides a practical and high-performance focusing solution capable of enhancing the reliability, throughput, and diagnostic utility of portable retinal imaging in large-scale screening applications. Future efforts will be directed towards system cost optimization and performance adaptation for diverse ocular conditions.

Objective: This study characterizes the demographic and clinical profiles of patients undergoing unilateral biportal endoscopic spine surgery (UBE ESS) for lumbar decompression/discectomy at a tertiary hospital in Singapore. It examines service implementation across multiple senior surgeons, evaluates preliminary clinical outcomes, and describes the learning curve observed during early adoption among surgeons already experienced in minimally invasive spine surgery, benchmarked against international standards. In the context of increasing global uptake of endoscopic techniques, this work provides evidence to inform institutional adoption and surgeon training. This analysis forms part of a multi-paper series comparing surgeon experience and patient outcomes between conventional minimally invasive approaches and UBE ESS for lumbar decompression/discectomy. Methods: We conducted a retrospective review of 111 patients who underwent UBE lumbar decompression/discectomy at a public tertiary hospital between October 2022 and April 2024. Data on patient demographics, comorbidities, presenting symptoms, operative details, and clinical outcomes, including visual analogue scale (VAS) scores and 36-Item Short Form Health Survey (SF-36) health domains, were analyzed using appropriate statistical methods. Results: The mean patient age was 56.8 years, with a slight female predominance (54.1%). Statistically significant improvements were observed in VAS scores for both back and leg pain (p<0.05), alongside significant gains in SF-36 domains including physical functioning, bodily pain, vitality, and social functioning. Operative times decreased progressively with increasing case volume, consistent with the presence of a procedural learning curve. Conclusion UBE ESS for lumbar decompression/discectomy is a safe and efficacious technique that can be successfully adopted by spinal surgeons with prior minimally invasive surgical experience. Operative time demonstrates a meaningful reduction once the initial learning curve has been overcome. ESS provides a reproducible option for the treatment of degenerative lumbar spine disease in the tertiary hospital setting in Singapore, with outcomes comparable to established international benchmarks. Future work will include long-term follow-up of this patient cohort and direct comparison with conventional minimally invasive techniques in subsequent studies.

Objective: Spinal stenosis and degenerative spinal disorders are increasingly prevalent and have a substantial impact on quality of life. Surgical decompression, performed using either open microscopic or endoscopic approaches, remains a cornerstone of management for these conditions. This study examines evolving trends in single-level lumbar spinal decompression procedures performed at a tertiary academic hospital in Singapore. Methods: A retrospective observational study was conducted involving 588 patients who underwent single-level spinal decompression between 2021 and 2024, including endoscopic spine surgery (ESS; n=364) and microdecompression (n=224). Primary outcome measures were changes in 36-Item Short Form Health Survey (SF-36) and visual analogue scale (VAS) scores at 3 months, 6 months, and 2 years postoperatively. Secondary outcomes included length of hospital stay, reoperation rates, and operative time. Patient demographics, spinal level and pathology characteristics, surgical techniques, and postoperative outcomes were analyzed. Difference-in-differences (DID) analysis was used to compare outcomes between the 2 groups. Results: Both groups demonstrated significant postoperative improvements in SF-36 and VAS scores. At 2 years, Short Form Health Survey physical function (SFPF) scores improved in the endoscopic group (mean difference [MD], 18.6; standard deviation [SD], 21.7; p=0.064) and in the open microscopic group (MD, 36.7; SD, 20.9; p=0.007), with a non-significant DID of -18.1 (p=0.155). No DID comparisons across SF-36 domains reached statistical significance. Mean operative time for endoscopic procedures decreased from 249 minutes in 2022 to 145 minutes in 2024, reflecting a procedural learning curve. Surgeons with higher endoscopic caseloads exhibited greater improvements in functional outcomes. Conclusion Both endoscopic and open microscopic decompression achieve comparable short- and long-term clinical outcomes. ESS provides similar effectiveness while being associated with shorter recovery periods and reduced hospital stay. Further research is warranted to identify factors contributing to incomplete symptom resolution or the need for revision surgery.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Oncological microdissection testicular sperm extraction (onco-micro-TESE) represents a significant breakthrough for patients with nonobstructive azoospermia (NOA) and a concomitant in situ testicular tumor, to be managed at the time of sperm retrieval. Onco-micro-TESE addresses the dual objectives of treating both infertility and the testicular tumor simultaneously. The technique is intricate, necessitating a comprehensive understanding of testicular anatomy, physiology, tumor biology, and advanced microsurgical methods. It aims to carefully extract viable spermatozoa while minimizing the risk of tumor dissemination. This review encapsulates the procedural intricacies, evaluates success determinants, including tumor pathology and spermatogenic tissue health, and discusses the implementation of imaging techniques for enhanced surgical precision. Ethical considerations are paramount, as the procedure implicates complex decision-making that weighs the potential oncological risks against the profound desire for fatherhood using the male gametes. The review aims to provide a holistic overview of onco-micro-TESE, detailing methodological advances, clinical outcomes, and the ethical landscape, thus offering an indispensable resource for clinicians navigating this multifaceted clinical scenario.
Humans ; Male ; Azoospermia/therapy* ; Testicular Neoplasms/pathology* ; Sperm Retrieval ; Microdissection/methods* ; Testis/surgery*

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Sepsis is a systemic inflammatory response caused by severe infection or trauma, and is one of the common causes of acute lung injury(ALI) and acute respiratory distress syndrome(ARDS). Sepsis-acute lung injury(SALI) is a critical clinical condition with high morbidity and mortality. Its pathogenesis is complex and not yet fully understood, and there is currently a lack of targeted and effective treatment options. Pyroptosis, a novel form of programmed cell death, plays a key role in the pathological process of SALI by activating inflammasomes and releasing inflammatory factors, making it a potential therapeutic target. In recent years, the role of traditional Chinese medicine(TCM) in regulating signaling pathways related to pyroptosis through multi-components and multi-targets has attracted increasing attention. TCM may intervene in pyroptosis by inhibiting the activation of NLRP3 inflammasomes and regulating the expression of Caspase family proteins, thus alleviating inflammatory damage in lung tissues. This paper systematically reviews the molecular regulatory network of pyroptosis in SALI and explores the potential mechanisms and research progress on TCM intervention in cellular pyroptosis. The aim is to provide new ideas and theoretical support for basic research and clinical treatment strategies of TCM in SALI.
Pyroptosis/drug effects* ; Humans ; Sepsis/genetics* ; Acute Lung Injury/physiopathology* ; Animals ; Drugs, Chinese Herbal/therapeutic use* ; Medicine, Chinese Traditional ; Inflammasomes/metabolism* ; NLR Family, Pyrin Domain-Containing 3 Protein/genetics*