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： The aim of this study is to explore the predictive value of biparametric magnetic resonance imaging(bpMRI)for pelvic lymph node metastasis in prostate cancer patients with PSA≤20 μg/L and establish a nomogram. Methods： The imaging data and clinical data of 363 patients undergoing radical prostatectomy and pelvic lymph node dissection in the First Affiliated Hospital of Nanjing Medical University from July 2018 to December 2023 were retrospectively analyzed. Univariate analysis and multivariate logistic regression were used to screen independent risk factors for pelvic lymph node metastasis in prostate cancer, and a nomogram of the clinical prediction model was established. Calibration curves were drawn to evaluate the accuracy of the model. Results： Multivariate logistic regression analysis showed extrocapusular extension (OR=8.08,95%CI=2.62－24.97, P<0.01), enlargement of pelvic lymph nodes (OR=4.45,95%CI=1.16－17.11,P=0.030), and biopsy ISUP grade(OR=1.97,95%CI=1.12－3.46, P=0.018)were independent risk factors for pelvic lymph node metastasis. The C-index of the prediction model was 0.834, which indicated that the model had a good prediction ability. The actual value of the model calibration curve and the prediction probability of the model fitted well, indicating that the model had a good accuracy. Further analysis of DCA curve showed that the model had good clinical application value when the risk threshold ranged from 0.05 to 0.70.Conclusion： For prostate cancer patients with PSA≤20 μg/L, bpMRI has a good predictive value for the pelvic lymph node metastasis of prostate cancer with extrocapusular extension, enlargement of pelvic lymph nodes and ISUP grade≥4.
Humans ; Male ; Prostatic Neoplasms/diagnostic imaging* ; Lymphatic Metastasis ; Retrospective Studies ; Nomograms ; Prostate-Specific Antigen/blood* ; Lymph Nodes/pathology* ; Pelvis ; Predictive Value of Tests ; Prostatectomy ; Lymph Node Excision ; Risk Factors ; Magnetic Resonance Imaging ; Logistic Models ; Middle Aged ; Aged

Objective:To develop an artificial intelligence (AI)-based platelet review strategy to identify abnormal platelet histograms with no significant difference between initial impedance platelet count (PLT-I) and PLT-F results.Methods:This study included 5 119 routine blood analysis in Nanfang Hospital of Southern Medical University and its Ganzhou branch from July 2023 and March 2024. Specimens exhibiting abnormal platelet histograms and an initial platelet count >40×10?/L underwent review using the fluorescent platelet count (PLT-F) channel. Consistency of the results was defined as a difference between impedance platelet count (PLT-I) and PLT-F less than ±20% of the PLT-F results. A deep learning model was developed using platelet and red blood cell histogram data from a training set of 3 807 specimens. The model′s diagnostic performance was evaluated on an independent external validation set ( n=805) using receiver operating characteristic (ROC) curve analysis. Changes in the number of reviewed samples and sample turnaround time were analyzed to assess its clinical utility. Results:The deep learning model based on platelet and red blood cell histograms achieved an area under the ROC curve (AUC) of 0.854 in the training set. At a cutoff value of 0.1, the sensitivity was 0.954 and specificity was 0.358. The model could reduce review by 16.80% (190/1 131). In the validation set, the AUC was 0.805, with a sensitivity of 0.955 and specificity of 0.307, corresponding to a reduction of 17.41% (47/270) in reviewed specimens.Conclusion:The platelet review prediction model developed based on deep learning technology can efficiently identify samples with consistent results before and after review, reducing unnecessary reviews and shortening specimen testing time, thereby improving the efficiency of platelet test.

Objective:To investigate the prevalence and severity of symptom burden among long-term survivors of oropharyngeal cancer after radiotherapy, to identify core symptom clusters, and to explore their correlation with quality of life.Methods:A previous retrospective study was conducted by the Cancer Hospital, Chinese Academy of Medical Sciences on patients with oropharyngeal cancer who underwent radiotherapy between January 2010 and December 2020. Patients who were still alive as of December 2023 were further followed and analyzed. From December 2023 to August 2024, symptom burden and quality of life were assessed using the Chinese version of the MD Anderson Symptom Inventory–Head and Neck Module (MDASI-HN) and the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire (EORTC QLQ). Exploratory factor analysis (principal component analysis with Promax rotation) were used to identify symptom clusters. Spearman correlation analysis was performed to explore the relationship between total symptom cluster scores and standardized domain scores of quality of life. Multivariate linear regression analysis was further employed to determine the relationship between identified symptom clusters and overall quality of life.Results:A total of 273 patients were included, with a median follow-up duration of 6.2 years (range: 3.5-14.5 years) and a median age of 61 years (range: 27-88 years) at follow-up. The top 5 incidence rates of symptom reported by patients were mucus problems in the mouth or throat (147 cases, 53.8%), dental or gum issues (143 cases, 52.4%), xerostomia (140 cases, 51.3%), difficulty swallowing or chewing (95 cases, 34.8%), and taste disturbance (79 cases, 28.9%). Among them, xerostomia was the most serious symptom. The most frequently reported interference was impact on work (including household chores) (55 cases, 20.1%). Exploratory factor analysis identified 3 symptom clusters: fatigue-nausea cluster, eating-voice cluster, and xerostomia-sleep cluster, all of which were significantly correlated with lower overall quality of life of patients (all P<0.001). Conclusion:Long-term survivors of oropharyngeal cancer after radiotherapy experience substantial symptom burden. The fatigue-nausea, eating-voice, and xerostomia-sleep clusters are the core symptom clusters impacting quality of life.

This study was aimed at identifying the pathogenic bacteria responsible for the death of a young tiger at the Fujian Meihua Mountain South China Tiger Breeding Research Institute.Tissue samples from the lungs,liver,and intestines of the deceased tiger were collected,and the bacteria were cultured inasterile environment.The bacterial strains were characterized according to their morphological and molecular biological properties,including assessment of virulence genes and antibiotic resistance genes,mouse lethality tests,and antibiotic susceptibility evaluations.A predominant bacterial strain isolated from the liver of the deceased tiger was identified as enterotoxigenic Escherichia coli(ETEC)strain Tiger22513F.Phylogenetic analysis of the 16S rRNA gene revealed that the Tiger22513F strain exhibited close genetic similarity to the reference strain ETEC(MF919609.1),with 99.9%nucleotide similarity,and resided on the same evolutionary branch.The Tiger22513F strain contained 11 antibiotic resistance genes(tetA,sul1,sul3,cmlA,floR,blaTEM,blaSHV,blaCMY-2,qnrA,qnrS,and qnrD)along with five virulence genes(VT1,fyuA,tsh,iucD,and ST).Mouse lethality tests indicated significant pathogenicity toward mice,affecting primarily the lungs,liver,and intestines.Antibiotic susceptibility testing demonstrated that this strain exhibited resistance to various classes of beta-lactam antibiotics,as well as quinolones and aminoglycosides.This investigation successfully isolated a multi-drug resistant enterotoxigenic Escherichia coli strain with pronounced pathogenicity from the liver of a deceased tiger;thus providing valuable scientific insights for clinical diagnosis,as well as prevention and control measures,against ETEC infections in South China tigers.

This study was aimed at identifying the pathogenic bacteria responsible for the death of a young tiger at the Fujian Meihua Mountain South China Tiger Breeding Research Institute.Tissue samples from the lungs,liver,and intestines of the deceased tiger were collected,and the bacteria were cultured inasterile environment.The bacterial strains were characterized according to their morphological and molecular biological properties,including assessment of virulence genes and antibiotic resistance genes,mouse lethality tests,and antibiotic susceptibility evaluations.A predominant bacterial strain isolated from the liver of the deceased tiger was identified as enterotoxigenic Escherichia coli(ETEC)strain Tiger22513F.Phylogenetic analysis of the 16S rRNA gene revealed that the Tiger22513F strain exhibited close genetic similarity to the reference strain ETEC(MF919609.1),with 99.9%nucleotide similarity,and resided on the same evolutionary branch.The Tiger22513F strain contained 11 antibiotic resistance genes(tetA,sul1,sul3,cmlA,floR,blaTEM,blaSHV,blaCMY-2,qnrA,qnrS,and qnrD)along with five virulence genes(VT1,fyuA,tsh,iucD,and ST).Mouse lethality tests indicated significant pathogenicity toward mice,affecting primarily the lungs,liver,and intestines.Antibiotic susceptibility testing demonstrated that this strain exhibited resistance to various classes of beta-lactam antibiotics,as well as quinolones and aminoglycosides.This investigation successfully isolated a multi-drug resistant enterotoxigenic Escherichia coli strain with pronounced pathogenicity from the liver of a deceased tiger;thus providing valuable scientific insights for clinical diagnosis,as well as prevention and control measures,against ETEC infections in South China tigers.

Objective:To develop an artificial intelligence (AI)-based platelet review strategy to identify abnormal platelet histograms with no significant difference between initial impedance platelet count (PLT-I) and PLT-F results.Methods:This study included 5 119 routine blood analysis in Nanfang Hospital of Southern Medical University and its Ganzhou branch from July 2023 and March 2024. Specimens exhibiting abnormal platelet histograms and an initial platelet count >40×10?/L underwent review using the fluorescent platelet count (PLT-F) channel. Consistency of the results was defined as a difference between impedance platelet count (PLT-I) and PLT-F less than ±20% of the PLT-F results. A deep learning model was developed using platelet and red blood cell histogram data from a training set of 3 807 specimens. The model′s diagnostic performance was evaluated on an independent external validation set ( n=805) using receiver operating characteristic (ROC) curve analysis. Changes in the number of reviewed samples and sample turnaround time were analyzed to assess its clinical utility. Results:The deep learning model based on platelet and red blood cell histograms achieved an area under the ROC curve (AUC) of 0.854 in the training set. At a cutoff value of 0.1, the sensitivity was 0.954 and specificity was 0.358. The model could reduce review by 16.80% (190/1 131). In the validation set, the AUC was 0.805, with a sensitivity of 0.955 and specificity of 0.307, corresponding to a reduction of 17.41% (47/270) in reviewed specimens.Conclusion:The platelet review prediction model developed based on deep learning technology can efficiently identify samples with consistent results before and after review, reducing unnecessary reviews and shortening specimen testing time, thereby improving the efficiency of platelet test.

Objective:To investigate the prevalence and severity of symptom burden among long-term survivors of oropharyngeal cancer after radiotherapy, to identify core symptom clusters, and to explore their correlation with quality of life.Methods:A previous retrospective study was conducted by the Cancer Hospital, Chinese Academy of Medical Sciences on patients with oropharyngeal cancer who underwent radiotherapy between January 2010 and December 2020. Patients who were still alive as of December 2023 were further followed and analyzed. From December 2023 to August 2024, symptom burden and quality of life were assessed using the Chinese version of the MD Anderson Symptom Inventory–Head and Neck Module (MDASI-HN) and the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire (EORTC QLQ). Exploratory factor analysis (principal component analysis with Promax rotation) were used to identify symptom clusters. Spearman correlation analysis was performed to explore the relationship between total symptom cluster scores and standardized domain scores of quality of life. Multivariate linear regression analysis was further employed to determine the relationship between identified symptom clusters and overall quality of life.Results:A total of 273 patients were included, with a median follow-up duration of 6.2 years (range: 3.5-14.5 years) and a median age of 61 years (range: 27-88 years) at follow-up. The top 5 incidence rates of symptom reported by patients were mucus problems in the mouth or throat (147 cases, 53.8%), dental or gum issues (143 cases, 52.4%), xerostomia (140 cases, 51.3%), difficulty swallowing or chewing (95 cases, 34.8%), and taste disturbance (79 cases, 28.9%). Among them, xerostomia was the most serious symptom. The most frequently reported interference was impact on work (including household chores) (55 cases, 20.1%). Exploratory factor analysis identified 3 symptom clusters: fatigue-nausea cluster, eating-voice cluster, and xerostomia-sleep cluster, all of which were significantly correlated with lower overall quality of life of patients (all P<0.001). Conclusion:Long-term survivors of oropharyngeal cancer after radiotherapy experience substantial symptom burden. The fatigue-nausea, eating-voice, and xerostomia-sleep clusters are the core symptom clusters impacting quality of life.

Background and objective The incidence rate of lung cancer in Xuanwei has been continuously in-creasing in recent years,and it also features high incidence across all age groups and high mortality rates among female lung cancer patients.Therefore,the search for more stable biomarkers for the diagnosis of Xuanwei lung cancer holds tremendous clinical application prospects.This study aims to explore the clinical application value of these four microRNAs(miRNAs)in-dividually and in combination with traditional lung cancer tumor markers in the detection and diagnosis of Xuanwei lung can-cer.Methods By detecting the expression levels of four miRNAs and five traditional lung cancer tumor markers in the serum of 45 Xuanwei lung cancer patients and healthy physical examination participants,the Logistic regression model was employed to comprehensively evaluate the sensitivity,specificity,diagnostic efficacy,and other relevant statistical indicators of the four miR-NAs in the diagnosis of Xuanwei lung cancer.Results Among the individual miRNAs,miR-4646-5p and miR-3654 showed significant differences in expression levels between the Xuanwei lung cancer group and the control group(P＜0.05).miR-3654 demonstrated the best diagnostic performance with a sensitivity of 86.7％ ,specificity of 82.2％ ,and an area under the curve of 0.847.Combining miR-3654 with miR-4646-5p and carcinoembryonic antigen(CEA)resulted in the highest diagnostic ef-ficacy for Xuanwei lung cancer,with a sensitivity of 73.3％ ,specificity of 93.3％ ,area under the curve of 0.901,and a positive predictive value of 91.7％ .Conclusion Among the four miRNAs,serum miR-3654 exhibits the best diagnostic efficacy for Xuanwei lung cancer.Combined with miR-4646-5p and CEA,its diagnostic value for Xuanwei lung cancer can be effectively enhanced,making it a promising screening indicator for Xuanwei lung cancer.

Humans ; Vaccines, Inactivated ; COVID-19/prevention & control* ; Vaccination ; Viral Vaccines ; Immunity ; Antibodies, Viral