False-negative Cases Research Articles

Clinically Significant Prostate Cancer Prediction Using Multimodal Deep Learning with Prostate-Specific Antigen Restriction

Prostate cancer (PCa) is a clinically heterogeneous disease. Predicting clinically significant PCa with low–intermediate prostate-specific antigen (PSA), which often includes aggressive cancers, is imperative. This study evaluated the predictive accuracy of deep learning analysis using multimodal medical data focused on clinically significant PCa in patients with PSA ≤ 20 ng/mL. Our cohort study included 178 consecutive patients who underwent ultrasound-guided prostate biopsy. Deep learning analyses were applied to predict clinically significant PCa. We generated receiver operating characteristic curves and calculated the corresponding area under the curve (AUC) to assess the prediction. The AUC of the integrated medical data using our multimodal deep learning approach was 0.878 (95% confidence interval [CI]: 0.772–0.984) in all patients without PSA restriction. Despite the reduced predictive ability of PSA when restricted to PSA ≤ 20 ng/mL (n = 122), the AUC was 0.862 (95% CI: 0.723–1.000), complemented by imaging data. In addition, we assessed clinical presentations and images belonging to representative false-negative and false-positive cases. Our multimodal deep learning approach assists physicians in determining treatment strategies by predicting clinically significant PCa in patients with PSA ≤ 20 ng/mL before biopsy, contributing to personalized medical workflows for PCa management.

Pioneering molecular screening for cervical precursor lesions and cervical cancer in sera

Cervical cancer is a significant public health issue in Mexico and many developing countries. Early detection is crucial for combating this disease. The official screening test for cervical cancer is cytology, but this technique faces several barriers, including methodological, educational, and sociocultural challenges. Liquid-based cytology is an improved version of this test, however it does not address the aforementioned complications. Biomarkers for cervical precursor lesions and cervical cancer can improve timely detection of the disease. A previous study from our group identified four circulating human proteins as potential biomarkers for these conditions. For molecular screening, we selected GAPDH as the biomarker for cervical precursor lesions and HNRNPA1 as the biomarker for cervical cancer -chosen from the three previously identified options based on antibody availability- to be detected in sera. Participants underwent a comprehensive panel of tests, including liquid-based cytology, PCR detection of Human papillomavirus (HPV), colposcopy, and histopathology -when applicable-. The last two tests were used as references for determining sensitivity and specificity, with histopathology being the gold standard for cervical cancer diagnosis. All the participants successfully received colposcopies (n = 99) and only those women with visible or suspected cervical lesions/malignancies were biopsied (n = 62). A subset of randomly selected biopsies underwent p16INK4a immunohistochemistry (n = 36). This study compares the performance of liquid-based cytology with the molecular screening. With colposcopy as reference, liquid-based cytology showed 30% sensitivity and 96% specificity, while the molecular screening showed 90% sensitivity and 43% specificity. With histopathology as reference, liquid-based cytology showed 21% sensitivity and 93% specificity, while the molecular screening showed 85% sensitivity and 61% specificity. The molecular screening outperformed the liquid-based cytology in several areas, including detecting true-positive cases, reducing false-negative cases by 34.62%, application time, simplicity of result´s categories, and acceptance among participants. An ideal screening test requires high sensitivity, maintains moderate specificity, and minimizes false negatives. Our proposed screening test meets these criteria, making it an ideal complement -or alternative- for cervical cancer screening.

Evaluation of the clinical utility of NIPT-plus and analysis of adverse pregnancy outcomes.

To evaluate the performance of NIPT-plus in detecting fetal aneuploidies and CNVs and analyze the factors influencing adverse pregnancy outcomes. The retrospectively analyzed 8726 pregnant women who underwent NIPT-plus for fetal screening were classified into low- (who tested voluntarily) and high-risk (women with advanced age, abnormal ultrasound, abnormal serological screening, or a combination of indications) groups. Basic maternal information, prenatal findings, and pregnancy outcomes were recorded. NIPT-plus performance was assessed for various chromosomal abnormalities and the association between the fetal fraction and adverse pregnancy outcomes. Thirty-six (0.4%) patients had failed tests; 144 (1.65%) positive cases were detected, of which, 107 (74.31%) opted for invasive testing, and 51 were verified as true positives. The total positive predictive value was 45.45% and 48.65% in the low- and high-risk groups, respectively, and the difference was not significant. Among the subsequent cases with abnormal ultrasound monitoring, two false-negative cases were identified, and pathogenic CNV diagnosis was confirmed through amniocentesis, resulting in pregnancy termination. Fetal fraction was not associated with an increased adverse pregnancy outcome risk; however, ethnic differences may affect pregnancy outcomes. NIPT-plus technology use is no longer restricted to high-risk pregnant women, and it may produce false-positive results. The stakeholders should be aware of this limitation. The uncertainties and potential risks of the test results should be explained to the test takers to enable informed decision making and to minimize unnecessary anxiety and concerns. Ethnicity may influence adverse pregnancy outcomes in local multiracial settings.

Multi-centre post-mortem study on the use of coronary inflammation in CCTA to improve the diagnosis of the coronary related cause of death with histopathology validation

Abstract Background/Introduction A limitation of post-mortem computed tomography (PMCT) is the diagnostic accuracy of coronary related death, which constitute majority of investigations. Measurements using coronary inflammation with fat attenuation index (FAI) Score, clinical risk factors and coronary plaque metrics from CCTA to predict fatal cardiac events (AI-Risk) in living patients has not been used in post-mortem setting. Purpose This study investigates two new techniques to aid the diagnosis of coronary related death in the post-mortem setting: (1) minimally invasive interventional technique to perform PMCT; (2) diagnostic accuracy of FAI-assisted PMCT with histopathology validation. Method Adults with sudden death of unknown cause <48 hours from death were included. Ultrasound guided femoral access to deliver guidewire to the aortic root (Figure 1A), followed by inflation of aortic occlusion balloon and injection of contrast for angiogram. A cardiac radiologist performed the coronary and FAI analysis (1B &C). Two experienced radiologists reported the cause of death without/with aid of FAI score results. The technique was introduced at a second PMCT centre. Results PMCT analysis of 37 participants (27 males, 10 females, age range 42-98) – 15 minimally invasive PMCT, and 22 PMCT from previous study showed FAI score (1D) and AI-Risk (1E) were significantly higher in participants who had coronary related death vs non-coronary related causes. In the receiver operating characteristic (ROC, 1F) analysis, thresholding at 42% for AI-Risk and 34 for Mean FAI score, was able to detect all 18 true positives, 2 false positives (1xpulmonary embolism, and 1xdual pathology of ruptured abdominal aneurysm with severe coronary artery disease; these cases showed high FAI score and AI-Risk), and all true negatives. Both radiologists identified 17/18 coronary related death. There were 5 cases of false positives for coronary related death in Radiologist 1 (3 years’ experience); 8 false positives in Radiologists 2 (10 years’ experience). In non-coronary related deaths, Radiologist 1 identified 14/19 true negative cases, and Radiologist 2 identified 11/19 true negative cases. In all the false positive and negative cases, the radiologists’ diagnostic confidence level was either possible, or unascertain. With the aid of FAI score results, all the true coronary related deaths were identified and no false negative case. This method was introduced at a second centre with 108 PMCTs analysed. There were 40 cases of non-coronary death, and 68 cases of coronary related death on PMCT (imaging diagnosis only). In the non-coronary group, FAI-score assisted results showed 100% concordance. In the coronary group, there was 10% dis-concordance (7/68). Conclusion Minimally invasive PMCT with FAI score-assisted diagnosis has the potential to increase the diagnostic confidence and accuracy of coronary-related cause of death.

Limitations when using artificial intelligence services to analyze chest X-rays

Background: One of the first radiology areas in which artificial intelligence began to be used and is still actively used to this day is chest X-ray examination. However, when interpreting these studies using artificial intelligence, radiologists still face a number of limitations on a daily basis that must be taken into account when making a medical opinion and which developers need to pay attention to in order to further improve the algorithms to increase their efficiency. Aims: Identification of limitations in the use of currently available artificial intelligence services for chest X-ray examinations and identification of promising directions for their further development. Materials and methods: A retrospective analysis of 155 cases of disagreement between the results of conclusions of artificial intelligence services and medical opinions when analyzing chest X-ray examinations was carried out. All cases included in the study were obtained from the Unified Radiological Information Service of the Unified Medical Information and Analytical System of Moscow. Results: Among the 155 analyzed cases of disagreement, 48 (31.0%) were false positives and 78 (50.3%) were false negatives. The remaining 29 (18.7%) cases were excluded from further study because they turned out to be true positive (27) or true negative (2). Among the 48 false-positive cases, the majority (93.8%) was due to the fact that the artificial intelligence service mistook normal anatomical structures of the chest (97.8% of cases) or a catheter shadow (2.2% of cases) for pneumothorax. Among false-negative studies, the proportion of missed clinically significant pathologies was 22.0%. Almost half of these cases (44.4%) were associated with missed lung nodes. The most common clinically insignificant pathology was calcifications in the lungs (60.9%). Conclusions: On the part of AI services, there was a tendency towards overdiagnosis. All false-positive cases were associated with erroneous detection of clinically significant pathology: pneumothorax, lung nodules, and pulmonary consolidation. Among false-negative cases, the proportion of missing clinically significant pathology was small and amounted to less than one-fourth.

Defining sensitivity and specificity for quality control of organized cancer screening using Prefectural Cancer Registry data in Japan

Objective To decrease cancer mortality by implementing cancer screening programs, rigorous quality control measures that utilize standardized indicators are imperative. In Japan, although each municipality performing cancer screening programs implements quality control for their programs using the checklist authorized by the Ministry of Health, Labour and Welfare, compliance with all the items listed is not possible because calculating sensitivity and specificity using cancer registry data is difficult under these circumstances. This report elucidates the methodology for calculating indicators, including sensitivity and specificity, by delineating the parameters of false-negative cases within population-based cancer screening programs in Japan. Furthermore, the inherent challenges associated with ensuring the quality control of cancer screening procedures are expounded upon in this report.Method Data from the Prefectural Cancer Registry of Japan and cancer screening records compiled by municipalities were used to differentiate true-positive, true-negative, false-positive, and false-negative cases based on the combination of screening test outcomes and subsequent cancer incidence.Results A false-negative case was defined as an examinee who received a cancer diagnosis within one year after undergoing the screening test, notwithstanding the negative judgment of the cancer screening decision. The duration for judgment of true-positive, true-negative, and false-negative cases was also extended to one year. Cancer identification after cancer screening was ascertained using data from the Prefectural Cancer Registry, ensuring uniform categorization of the four cases. Subsequently, sensitivity and specificity values were calculated for municipalities conducting cancer screening programs.Conclusion Sensitivity and specificity are indispensable metrics for the inherent quality control of cancer screening because these parameters directly assess the efficacy of screening tests. The anticipated increase in the number of municipalities engaged in comprehensive quality control of cancer screening in Japan is poised to enhance the efficiency of cancer control policies. This augmentation will be accomplished through the meticulous utilization of the sensitivity and specificity values elucidated in the present report. The forthcoming challenges involve the proliferation of medical institutes reporting their adherence to the checklist stipulated by the National Cancer Center of Japan and the widespread dissemination of fundamental knowledge pertaining to cancer screening.

Comparison of Sentinel Lymph Node Sampling with Positron Emission Tomography in the Evaluation of Axillary Lymph Node Involvement in Breast Cancer Patients

Objectives Axillary lymph node status is the most important prognostic factor in breast cancer patients.: This study was carried out to evaluate the diagnostic accuracy of FDG-PET/CT as a noninvasive technique and intraoperative frozen biopsy of sentinel lymphadenectomy (SLNB) in detecting axillary lymph node metastasis. Materials and Methods This study was carried out retrospectively on 44 patients diagnosed with breast cancer, who underwent preoperative FDGPET/ CT imaging and intraoperative SLNB, at the General Surgery Clinic of Istanbul Medeniyet University Göztepe Training and Research Hospital. The axilla was clinically negative in all patients. Preopative FDG-PET/CT imaging and intraoperative SLNB were performed. FDG-PET/CT results were compared with the histopathological results of SLNB and axillary lymph node dissection (ALND). Results According to the pathology results of axillary dissection, metastatic nodes were detected in 22 of 44 cases, and FDG-PET/ CT imaging gave false-negative results in 10. The number of false negative cases of SLNB was 3; Axillary involvement was detected as a result of pathology in one of them, while the others were evaluated as skip metastases. The sensitivity of SLNB and FDG-PET/CT imaging was measured as 86.3% and 54.5%. The specificity values were 95.4% and 100%. FDG-PET/CT imaging has low sensitivity; specificity and positive predictive value were at acceptable levels Conclusion: The sensitivity of FDG-PET/CT imaging is low in detecting axillary involvement, and SLNB is needed in those with negative axillary involvement in FDG-PET/ CT imaging. In our study, SLNB examination was superior to FDG-PET/CT imaging in detecting axillary nodal status. Bangladesh Journal of Medical Science Vol. 23 No. 04 October’24 Page : 1083-1094

An expert rule-based approach for identifying infantile-onset Pompe disease patients using retrospective electronic health records

Pompe disease (OMIM #232300), a rare genetic disorder, leads to glycogen buildup in the body due to an enzyme deficiency, particularly harming the heart and muscles. Infantile-onset Pompe disease (IOPD) requires urgent treatment to prevent mortality, but the unavailability of these methods often delays diagnosis. Our study aims to streamline IOPD diagnosis in the UAE using electronic health records (EHRs) for faster, more accurate detection and timely treatment initiation. This study utilized electronic health records from the Abu Dhabi Healthcare Company (SEHA) healthcare network in the UAE to develop an expert rule-based screening approach operationalized through a dashboard. The study encompassed six diagnosed IOPD patients and screened 93,365 subjects. Expert rules were formulated to identify potential high-risk IOPD patients based on their age, particular symptoms, and creatine kinase levels. The proposed approach was evaluated using accuracy, sensitivity, and specificity. The proposed approach accurately identified five true positives, one false negative, and four false positive IOPD cases. The false negative case involved a patient with both Pompe disease and congenital heart disease. The focus on CHD led to the overlooking of Pompe disease, exacerbated by no measurement of creatine kinase. The false positive cases were diagnosed with Mitochondrial DNA depletion syndrome 12-A (SLC25A4 gene), Immunodeficiency-71 (ARPC1B mutation), Niemann–Pick disease type C (NPC1 gene mutation leading to frameshift), and Group B Streptococcus meningitis. The proposed approach of integrating expert rules with a dashboard facilitated efficient data visualization and automated patient screening, which aids in the early detection of Pompe disease. Future studies are encouraged to investigate the application of machine learning methodologies to enhance further the precision and efficiency of identifying patients with IOPD.

Artificial intelligence as an initial reader for double reading in breast cancer screening: a prospective initial study of 32,822 mammograms of the Egyptian population

BackgroundAlthough artificial intelligence (AI) has potential in the field of screening of breast cancer, there are still issues. It is vital to make sure AI does not overlook cancer or cause needless recalls. The aim of this work was to investigate the effectiveness of indulging AI in combination with one radiologist in the routine double reading of mammography for breast cancer screening. The study prospectively analyzed 32,822 screening mammograms. Reading was performed in a blind-paired style by (i) two radiologists and (ii) one radiologist paired with AI. A heatmap and abnormality scoring percentage were provided by AI for abnormalities detected on mammograms. Negative mammograms and benign-looking lesions that were not biopsied were confirmed by a 2-year follow-up.ResultsDouble reading by the radiologist and AI detected 1324 cancers (6.4%); on the other side, reading by two radiologists revealed 1293 cancers (6.2%) and presented a relative proportion of 1·02 (p < 0·0001). At the recall stage, suspicion and biopsy recommendation were more presented by the AI plus one radiologist combination than by the two radiologists. The interpretation of the mammogram by AI plus only one radiologist showed a sensitivity of 94.03%, a specificity of 99.75%, a positive predictive value of 96.571%, a negative predictive value of 99.567%, and an accuracy of 99.369% (from 99.252 to 99.472%). The positive likelihood ratio was 387.260, negative likelihood ratio was 0.060, and AUC “area under the curve” was 0.969 (0.967–0.971).ConclusionsAI could be used as an initial reader for the evaluation of screening mammography in routine workflow. Implementation of AI enhanced the opportunity to reduce false negative cases and supported the decision to recall or biopsy.

Prediction of Upper Limb Motor Recovery by the PREP2 Algorithm in a Nonselected Population: External Validation and Influence of Cognitive Syndromes

Background Early prediction of poststroke motor recovery is challenging in clinical settings. The Prediction recovery potential (PREP2) algorithm is the most accurate approach for prediction of Upper Limb function available to date but lacks external validation. Objectives (i) To externally validate the PREP2 algorithm in a prospective cohort, (ii) to study the characteristics of patients misclassified by the algorithm, and (iii) to compare the performance according to the presence of cognitive syndromes (aphasia, neglect, cognitive disorders). Methods We enrolled 143 patients with stroke and upper extremity weakness persistent at Day 3. Evaluation to predict the recovery status according to the PREP2 algorithm included age, SAFE and NIHSS scores at Day 3 and transcranial magnetic stimulation to determine the presence of the motor-evoked potential before day seven. Actual recovery (excellent, good, limited, or poor) was defined based on the Action Research Arm test score at 3 months. Accuracy was computed by comparing the predictions of the PREP2 and the actual category of the patient. Additionally, to investigate misclassifications and the impact of cognitive syndromes, we recorded SAFE and NIHSS scores at Day 7, the Montreal Cognitive Assessment (MoCA) score, the presence of aphasia and neglect and Magnetic Resonance Imaging was used to evaluate the corticospinal tract lesion load. Results The PREP2 algorithm showed a very good predictive value with 78% accuracy [95% CI: 71.2%-86.1%], especially for the extreme categories of recovery (EXCELLENT 87.5% [95% CI: 78.9%-96.2%] and POOR 94.9% [95% CI: 87.9%-100%]), and only 46.5% [95% CI: 19.05%-73.25%] for the GOOD category and even worse than chance for the LIMITED category 0%. Pessimistic predictions (false-negative cases) had a drastic improvement in the SAFE score acutely compared to that of well-predicted patients with unfavorable recovery (P < 001). The predictive value of PREP2 decreased significantly when patients had cognitive disorders (MoCA score <24) versus not (69.4% [95% CI: 52.8%-86.1%] vs 93.1% [95% CI: 83.9%-100%], P = .01). Conclusion Our study provides an external validation of the PREP2 algorithm in a prospective population and underlines the importance of taking into account cognitive syndromes in motor recovery prediction.

Real-Time Gastric Juice Analysis to Rule Out the Presence of Autoimmune Gastritis: A Case-Control Study

Background: Autoimmune gastritis (AIG) is an infrequent disease predisposing to both neuroendocrine tumours and cancer. This study aimed to evaluate whether pH measurement of gastric juice allows accurate exclusion of the presence of AIG in real time so that gastric mucosa sampling on normal-appearing mucosa may be avoided. Methods: This study enrolled patients diagnosed with AIG and matched controls (ratio 1:5) who underwent upper endoscopy with standard gastric mucosa sampling and real-time, gastric juice pH assessment. A threshold of pH less than 4.5 was adopted as cut-off to rule out the presence of a feature of AIG. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), overall accuracy, positive likelihood ratio (LR+), and negative likelihood ratio (LR−) were calculated. Results: Data of 40 patients (M/F: 19/21; mean age: 58 years, range: 18–89) with AIG and 212 matched controls were evaluated. Among AIG patients, the feature of atrophy/metaplasia of the oxyntic mucosa was staged as mild in 9 cases, moderate in 9, and severe in the remaining 22 patients. Gastric juice analysis showed a pH value >4.5 in 29 (72.5%) patients and 12 (5.7%) controls. Sensitivity, specificity, accuracy, PPV, NPV, LR+, and LR− were 73% (95% CI = 0.57–0.84), 94% (95% CI = 0.90–0.97), 71% (95% CI = 0.64–0.74), 95% (95% CI = 0.93–0.97), 91% (95% CI = 0.87–0.95), 12.9 (95% CI = 7.19–23.03), and 0.29 (95% CI = 0.18–0.48), respectively. The histological assessment of false-negative cases showed the presence of only mild-moderate atrophy of oxyntic mucosa in 6 (54.5%) cases, and severe in the others. Conclusions: Our data found that real-time pH evaluation of gastric juice allows ruling out AIG with a very high NPV, but further studies are needed.

Combined use of the Montreal Cognitive Assessment and Symbol Digit Modalities Test improves neurocognitive screening accuracy after cardiac arrest: A validation sub-study of the TTM2 trial

AimTo assess the merit of clinical assessment tools in a neurocognitive screening following out-of-hospital cardiac arrest (OHCA). MethodsThe neurocognitive screening that was evaluated included the performance-based Montreal Cognitive Assessment (MoCA) and Symbol Digit Modalities Test (SDMT), the patient-reported Two Simple Questions (TSQ) and the observer-reported Informant Questionnaire on Cognitive Decline in the Elderly-Cardiac Arrest (IQCODE-CA). These instruments were administered at 6-months in the Targeted Hypothermia versus Targeted Normothermia after Out-of-Hospital Cardiac Arrest (TTM2) trial. We used a comprehensive neuropsychological test battery from a TTM2 trial sub-study as a gold standard to evaluate the sensitivity and specificity of the neurocognitive screening. ResultsIn our cohort of 108 OHCA survivors (median age = 62, 88% male), the most favourable cut-off scores were: MoCA < 26; SDMT z ≤ -1; IQCODE-CA ≥ 3.04. The MoCA (sensitivity 0.64, specificity 0.85) and SDMT (sensitivity 0.59, specificity 0.83) had a higher classification accuracy than the TSQ (sensitivity 0.28, specificity 0.74) and IQCODE-CA (sensitivity 0.42, specificity 0.60). When using the cut-points for MoCA or SDMT in combination to identify neurocognitive impairment, sensitivity improved (0.81, specificity 0.74), area under the curve = 0.77, 95% CI [0.69, 0.85]. The most common unidentified impairments were within the episodic memory and executive functions domains, with fewer false negative cases on the MoCA or SDMT combined. ConclusionThe MoCA and SDMT have acceptable diagnostic accuracy for screening for neurocognitive impairment in an OHCA population, and when used in combination the sensitivity improves. Patient and observer-reports correspond poorly with neurocognitive performance.ClinicalTrials.gov Identifier: NCT03543371.

Comparison of the modified Masood's scoring index versus international academy of cytology Yokohama system in the categorization of breast fine needle aspirates.

The Modified Masood Scoring Index (MMSI) categorizes breast fine needle aspirates into four categories non-proliferative breast diseases (PBD), PBD without atypia, PBD with atypia and carcinoma insitu/carcinoma. The International Academy of Cytology Yokohama System classifies the aspirates into five categories - inadequate, benign, atypical, suspicious, and malignant. Very few studies have been conducted so far to compare the diagnostic accuracy of this system. The objectives of the study were to classify breast fine needle aspirates according to the MMSI and IAC Yokohama and assess their risk of malignancy (ROM) and performance parameters. All breast FNAs received from June 2020 to May 2023 were classified according to the MMSI and IAC Yokohama. Using histopathological diagnosis as the gold standard, ROM and performance parameters were calculated. Out of the 559 breast fine needle aspirates, the histopathological correlation was available for 337 cases. The ROM for non-PBD, PBD without atypia, PBD with atypia and carcinoma insitu/carcinoma categories were 0%, 1.2%, 9.1% and 93%, respectively. The ROM for each of the categories of Yokohama system was 16.6%, 1.1%, 4.3%, 86.2% and 97.6%, respectively. The values for the sensitivity, specificity, PPV, NPV and diagnostic accuracy were slightly higher for IAC Yokohama (97.3%, 97.2%, 94.7%, 98.6% and 97.2%, respectively) than the MMSI (96.4%, 96.2%, 93%, 98.1% and 96.3%, respectively). Furthermore, another shortfall of MMSI is the inability to compare the Inadequate category of the Yokohama system. Overall Yokohama system proved to be a better system for categorizing breast lesions on FNAB as the scoring system provides more objective categorization and minimizes false positive and false negative cases.

A Review on Mastitis in Dairy Cows Research: Current Status and Future Perspectives

One of the most serious diseases affecting dairy cattle, causing significant losses both in breeding and economy, is mastitis, an inflammation of the mammary gland. Due to the economic importance of this issue, many research teams are striving to develop an easy-to-apply and, most importantly, effective method to prevent mastitis. The use of traditional methods for mastitis detecting and treating, as well as improvement in hygienic conditions, have not yielded the expected results in combating this disease combating. Currently, the main task is to find the tools that would allow for the rapid detection of mastitis and the improvement of udder health in cows while maintaining high milk production, which is essential for the profitability of dairy cattle farming. Accurate and rapid diagnostic tools, with the simultaneous capability of identifying pathogens, may help to reduce losses. Sufficient sensitivity and specificity for tests are required to minimize the number of false-positive and false-negative cases. Efforts are also being made to determine the optimal threshold value for detecting the disease at its earliest possible stage. The estimation of somatic cell count (SCC) as a phenotypic indicator of mastitis is widely used. A more precise parameter for accurately describing udder health is the differential somatic cell count (DSCC). The well-known California Mastitis Test (CMT) is an inexpensive, easy, and rapid method for mastitis detection useful on farms. The latest diagnostic methods for mastitis utilize tests based on the activity of N-acetyl-β-d-glucosaminidase (NAGase) or lactate dehydrogenase (LDH) as well as the determination of acute phase proteins (APPs) in blood serum and milk (such as haptoglobin, serum amyloid A, fibrinogen, and ceruloplasmin). Research also focuses on the genomic improvement of mastitis resistance in successive generations, and for this purpose, many quantitative trait loci (QTLs) and single nucleotide polymorphisms (SNPs) have been identified. In recent years, immunotherapy has become an increasingly common area of research, including vaccinations, T/B cell immunotherapy, RNA immunotherapy, epigenetic immunotherapy, stem cell therapy, and native secretory factors. An important aspect of the control of mastitis is the implementation of strategies that focus primarily on preventing the disease through appropriate breeding and farm management practices. In the forthcoming years, a significant challenge will be the development of universal diagnostic and therapeutic strategies that can be effectively implemented as alternatives to antibiotic therapy. Future research should prioritize the advancement of preventive and therapeutic techniques, such as immunotherapies, bacteriocins, herbal therapy, and nanoparticle technology.

Performance of the 2023 American College of Rheumatology/EULAR Classification Criteria for Antiphospholipid Syndrome in a Mexican Cohort.

The objective is to assess the performance of the 2023 American College of Rheumatology/EULAR classification criteria (2023 AECC) for antiphospholipid syndrome (APS) in a Mexican cohort. We enrolled patients with primary APS (PAPS) and secondary APS (SAPS) and a control group of nonautoimmune thrombophilia. We evaluated the fulfillment of the 2023 AECC and the 2006 revised Sapporo classification criteria (2006 RSCC) and their performance against the clinical diagnosis as the gold standard. The baseline Global APS Score (GAPSS) and the Damage Index for APS (DIAPS) at last follow-up were calculated. We included 85 patients with PAPS, 54 with SAPS, and 50 with thrombophilia. According to the 2023 AECC criteria, 69 patients (81.2%) with PAPS, 28 patients (51.9%) with SAPS, and none of the patients with thrombophilia met the criteria. When comparing true positive (TP) (n = 69) versus false negative (n = 16) cases within the PAPS group, TP cases exhibited a higher frequency of thrombotic manifestations and IgM anti-cardiolipin and IgG anti-β2-glycoprotein-I positivity. For PAPS, there was a correlation between the 2023 AECC score and both GAPSS (rho = 0.621, P < 0.0001) and DIAPS scores (rho = 0.433, P < 0.0001). When comparing the 2023 AECC with the 2006 RSCC, a lower sensitivity (81.2% vs 88.2%) but a higher specificity (100.0% vs 92.0%) was observed for PAPS. Similar findings were observed in SAPS. In both PAPS and SAPS, the 2023 AECC have higher specificity than the 2006 RSCC. The main feature of patients with PAPS according to the 2023 AECC was thrombosis. These criteria might identify patients at higher risk of thrombosis and damage accrual.

NIRO200NX: Reliable Monitoring System for Buried Deep Inferior Epigastric Perforator Flap.

The deep inferior epigastric perforator (DIEP) flap is a useful tool for breast reconstruction and tends to be transferred into the breast envelope as the buried flap from an aesthetic point of view. However, it is difficult to monitor the blood flow in the buried DIEP flap after reconstructive microsurgery. Near-infrared spectroscopy devices have recently been used for monitoring the blood flow of various organs. NIRO200NX (Hamamatsu Photonics) continuously measures the tissue oxygen index (TOI) and quickly reflects changes in flap blood flow. In this study, we investigated whether and how much the NIRO200NX applies to monitoring the blood flow of the buried flap. We included 156 patients who underwent breast reconstruction using a DIEP flap from October 2013 to May 2022, comprising 57 exposed and 99 buried-type DIEP flap cases. We measured TOI using NIRO200NX, in combination with conventional evaluation methods, including color check, pinprick test, and Doppler sound. A criterion of TOI 50 gave the best evaluations. All the 57 exposed-type flap cases showed no false evaluations, and NIRO200NX performed precise judgment. In 99 buried-type flap cases, NIRO200NX correctly evaluated 96 cases. For those buried-type cases, we found only two false-positive and one false-negative case. The misjudgments by NIRO200NX were likely caused by hematoma. We propose NIRO200NX as a reliable device for monitoring the blood flow of the DIEP flap and predicting the outcomes of breast reconstruction by the DIEP flap transfer.

Deep learning for contour quality assurance for RTOG 0933: In-silico evaluation

PurposeTo validate a CT-based deep learning (DL) hippocampal segmentation model trained on a single-institutional dataset and explore its utility for multi-institutional contour quality assurance (QA). MethodsA DL model was trained to contour hippocampi from a dataset generated by an institutional observer (IO) contouring on brain MRIs from a single-institution cohort. The model was then evaluated on the RTOG 0933 dataset by comparing the treating physician (TP) contours to blinded IO and DL contours using Dice and Haussdorf distance (HD) agreement metrics as well as evaluating differences in dose to hippocampi when TP vs. IO vs. DL contours are used for planning. The specificity and sensitivity of the DL model to capture planning discrepancies was quantified using criteria of HD > 7 mm and Dmax hippocampi > 17 Gy. ResultsThe DL model showed greater agreement with IO contours compared to TP contours (DL:IO L/R Dice 74%/73%, HD 4.86/4.74; DL:TP L/R Dice 62%/65%, HD 7.23/6.94, all p < 0.001). Thirty percent of contours and 53 % of dose plans failed QA. The DL model achieved an AUC L/R 0.80/0.79 on the contour QA task via Haussdorff comparison and AUC of 0.91 via Dmax comparison. The false negative rate was 17.2%/20.5% (contours) and 5.8% (dose). False negative cases tended to demonstrate a higher DL:IO Dice agreement (L/R p = 0.42/0.03) and better qualitative visual agreement compared with true positive cases. ConclusionOur study demonstrates the feasibility of using a single-institutional DL model to perform contour QA on a multi-institutional trial for the task of hippocampal segmentation.

Clinical application and evaluation of metagenomic next-generation sequencing in pathogen detection for suspected central nervous system infections

Central nervous system Infections (CNSIs) is a disease characterized by complex pathogens, rapid disease progression, high mortality rate and high disability rate. Here, we evaluated the clinical value of metagenomic next generation sequencing (mNGS) in the diagnosis of central nervous system infections and explored the factors affecting the results of mNGS. We conducted a retrospective study to compare mNGS with conventional methods including culture, smear and etc. 111 suspected CNS infectious patients were enrolled in this study, and clinical data were recorded. Chi-square test were used to evaluate independent binomial variables, taking p < 0.05 as statistically significant threshold. Of the 111 enrolled cases, 57.7% (64/111) were diagnosed with central nervous system infections. From these cases, mNGS identified 39.6% (44/111) true-positive cases, 7.2% (8/111) false-positive case, 35.1% (39/111) true-negative cases, and 18.0% (20/111) false-negative cases. The sensitivity and specificity of mNGS were 68.7% (44/64) and 82.9% (39/47), respectively. Compared with culture, mNGS provided a higher pathogen detection rate in CNSIs patients (68.7% (44/64) vs. 26.5% (17/64), p < 0.0001). Compared to conventional methods, positive percent agreement and negative percent agreement was 84.60% (44/52) and 66.1% (39/59) separately. At a species-specific read number (SSRN) ≥ 2, mNGS performance in the diagnosis of definite viral encephalitis and/or meningitis was optimal (area under the curve [AUC] 0.758, 95% confidence interval [CI] 0.663–0.854). In bacterial CNSIs patients with significant CSF abnormalities (CSF WBC > 300*106/L), the positive rate of CSF mNGS is higher. To sum up, conventional microbiologic testing is insufficient to detect all neuroinvasive pathogens, and mNGS exhibited satisfactory diagnostic performance in CNSIs and with an overall detection rate higher than culture (p < 0.0001).

A Five-Year Review of Newborn Screening for Spinal Muscular Atrophy in the State of Utah: Lessons Learned.

Spinal muscular atrophy (SMA) is an autosomal recessive condition characterized by alpha motor neuron degeneration in the spinal cord anterior horn. Clinical symptoms manifest in the first weeks to months of life in the most severe cases, resulting in progressive symmetrical weakness and atrophy of the proximal voluntary muscles. Approximately 95% of SMA patients present with homozygous deletion of the SMN1 gene. With multiple available therapies preventing symptom development and slowing disease progression, newborn screening for SMA is essential to identify at-risk individuals. From 2018 to 2023, a total of 239,844 infants were screened. 13 positive screens were confirmed to have SMA. An additional case was determined to be a false positive. We are not aware of any false-negative cases. All patients were seen promptly, with diagnosis confirmed within 1 week of the initial clinical visit. Patients were treated with nusinersen or onasemnogene abeparvovec. Treated patients with two copies of SMN2 are meeting important developmental milestones inconsistent with the natural history of type 1 SMA. Patients with 3-4 copies of SMN2 follow normal developmental timelines. Newborn screening is an effective tool for the early identification and treatment of patients with SMA. Presymptomatic treatment dramatically shifts the natural history of SMA, with most patients meeting appropriate developmental milestones. Patients with two copies of SMN2 identified through newborn screening constitute a neurogenetic emergency. Due to the complexities of follow-up, a multidisciplinary team, including close communication with the newborn screening program, is required to facilitate timely diagnosis and treatment.

A Working Conditions Warning Method for Sucker Rod Wells Based on Temporal Sequence Prediction

The warning of the potential faults occurring in the future in a sucker rod well can help technicians adjust production strategies in time. It is of great significance for safety during well production. In this paper, the key characteristic parameters of dynamometer cards were predicted by a temporal neural network to implement the warning of different working conditions which might result in failures. First, a one-dimensional damped-wave equation was used to eliminate the dynamic loads’ effect of surface dynamometer cards by converting them into down-hole dynamometer cards. Based on the down-hole dynamometer cards, the characteristic parameters were extracted, including the load change, the position of the valve opening and closing point, the dynamometer card area, and so on. The mapping relationship between the characteristic parameters and working conditions (classification model) was obtained by the Xgboost algorithm. Meanwhile, the noise in these parameters was reduced by wavelet transformation, and the rationality of the results was verified. Second, the Encoder–Decoder and multi-head attention structures were used to set up the time series prediction model. Then, the characteristic parameters were predicted in a sequence-to-sequence way by using historical characteristic parameters, date, and pumping parameters as input. At last, by inputting the predicted results into the classification model, a working conditions warning method was created. The results showed that noise reduction improved the prediction accuracy significantly. The prediction relative error of most characteristic parameters was less than 15% after noise reduction. In most working conditions, their F1 values were more than 85%. Most Recall values could be restored to over 90% of those calculated by real parameters, indicating few false negative cases. In general, the warning method proposed in this paper can predict faulty working conditions that may occur in the future in a timely manner.