Mean Sensitivity Research Articles

The association between airborne particulate matter (PM2.5) exposure level and primary open-angle glaucoma

The eye is vulnerable to the adverse effects of air pollution. Previous experimental study found that fine particulate matter (PM2.5) had a direct toxic effect on intraocular tissues. However, clinical evidence for the impact of air pollutants exposure on functional and structural changes in glaucoma remains scarce. A total of 120 patients with primary open-angle glaucoma (POAG) who met the inclusion criteria were included in this retrospective study. The standardized ophthalmic examination, such as intraocular pressure (IOP), visual field, optical coherence tomography, and comprehensive physical examination, were performed. The air pollution data, including PM2.5 concentration and air quality index (AQI), were collected. PM2.5 and AQI for the day of the medical examination, as well as one month, and three months before the medical examination date, were investigated. In our results, higher average exposure levels for one-month and three-month, were associated with increased IOP (r=0.229, P=0.013; r=0.204, P=0.028, respectively) and decreased visual field mean sensitivity (MS) (r=-0.212, P=0.037; r=-0.305, P=0.002, respectively). PM2.5 concentrations for the day of the medical examination was not significantly associated with ocular parameters. In multiple linear regression analysis adjusted for demographic and clinical factors, higher PM2.5 exposure for one month was associated with elevated IOP (P=0.040, β=0.173, 95 %CI=0.008–0.337). We also found an association between PM2.5 and MS (one-month exposure: β=-0.160, P=0.029; three-month exposure: β=-0.238, P=0.002). The logistic regression analysis found that three-month average PM2.5 exposure level was significantly associated with the disease severity (β=0.043, P=0.025, 95 %CI=1.005–1.084). In conclusion, this study is the first to investigate the relationship between air pollution and detailed ocular parameters of POAG patients in Shanghai over a three-year period, and to explore the effects of different exposure times of PM2.5 on glaucoma. This study found that PM2.5 exposure was correlated with elevated IOP and decreased MS. The one-month PM2.5 exposure level had the most significant effects on IOP. The three-month PM2.5 exposure level was an independent risk factor for POAG severity. Current evidence suggests there may be an association between PM2.5 exposure and POAG.

Fractional tumor burden maps increase the confidence of reading brain MR perfusion

Rationale and objectivesVarious methods exist to perform and post-process perfusion weighted MR imaging in the post-treatment imaging of glioma patients to differentiate tumor progression from tumor-related abnormalities. One of these post-processing methods produces ‘fractional tumor burden’ maps. This multi-reader study investigated the clinical feasibility of fractional tumor burden maps on real world data from radiological follow-up of high-grade astrocytoma patients. MethodsFive readers with background in radiology and varying levels of experience were tasked with assessing 30 astrocytoma and glioblastoma patients during one reader session. First, they were provided with a dataset of conventional MRI sequences, including perfusion MRI with regional cerebral blood volume maps. Then the dataset was expanded with a corresponding fractional tumor burden maps. Diagnostic accuracy, duration of post-processing, duration of the assessment of the fractional tumor burden maps, the diagnostic confidence reported by the readers and their diagnoses were recorded. Final diagnosis was determined by clinical and radiological follow-up and/or histopathological data used as gold standard. ResultsA mean sensitivity of 83.3 % and mean specificity of 55.1 % was obtained without the use of fractional tumor burden maps, whereas their additional of fractional tumor burden maps resulted in a mean sensitivity and specificity of 79.5 % and 54.2 %, respectively. Diagnostic accuracies with and without fractional tumor burden maps were not significantly different (Z = 0.76, p = 0.450). The median time spent post-processing was 313 s, while the median duration of the assessment of the FTB maps was 19 s. Interestingly, reader confidence increased significantly after adding the fractional tumor burden-maps to the assessment (Z = 454, p < 0.01). ConclusionsWhile the use of fractional tumor burden maps does not carry additional value in the radiological follow-up of post-operative high-grade astrocytoma and glioblastoma patients, it does give readers more confidence in their diagnosis.

Does the brain make prolactin?

The prolactin receptor (Prlr) is widely expressed in the brain, particularly in the hypothalamus. Prolactin also has an increasing range of well-characterised effects on central nervous system function. Because of this, over many years, there has been interest in whether the hormone itself is also expressed within the brain, perhaps acting as a neuropeptide to regulate brain function via its receptor in neurons. The aim of this invited review is to critically evaluate the evidence for brain production of prolactin. Unlike the evidence for the Prlr, evidence for brain prolactin is inconsistent and variable. A range of different antibodies have been used, each characterising a different distribution of prolactin-like immunoreactivity. Prolactin mRNA has been detected in the brain, but only at levels markedly lower than seen in the pituitary gland. Importantly, it has largely only been detected by highly sensitive amplification-based techniques, and the extreme sensitivity means there is a risk of false-positive data. Modern in situ hybridisation methods and single-cell RNA sequencing have not provided supporting evidence, but it is hard to prove a negative! Finally, I acknowledge and discuss the possibility that prolactin might be produced in the brain under specific circumstances, such as to promote a neuroprotective response to cell damage. Collectively, however, based on this analysis, I have formed the opinion that brain production of prolactin is unlikely, and even if occurs, it is of little physiological consequence. Most, if not all of the brain actions of prolactin can be explained by pituitary prolactin gaining access to the brain.

Censoring the Floor Effect in Long-Term Stargardt Disease Microperimetry Data Produces a Faster Rate of Decline

PURPOSETo evaluate progression rate estimation in long-term Stargardt disease microperimetry data by accounting for floor effect. DESIGNCohort study METHODSCross-sectional and longitudinal microperimetry data (Grid A: 18° diameter, Grid B: 6° diameter; MAIA microperimeter, dynamic range 0-36 dB) was extracted from patients with biallelic mutation in the ABCA4 gene. For each eye, mean sensitivity (MS) and responding point sensitivity (RPS) rates were extracted. Floor censored sensitivity (FCS) progression rate, which accounts for the floor effect at each locus by terminating calculation when scotoma was observed in 2 consecutive visits, was also calculated. In a subset of eyes with ≥1 scotomatous locus at baseline (Grid A), sensitivity progression of loci around the scotoma (edge of scotoma sensitivity, ESS) was examined against to other progression parameters. Paired t-test compared progression rate parameters across the same eyes. MAIN OUTCOME MEASURESMicroperimetry grid parameters at baseline and progression rates. RESULTSA total of 37 subjects with biallelic ABCA4 mutations and >2 years of longitudinal microperimetry data were included in the study. In Grid A, at baseline, the average MS and RPS were 16.5 ± 7.9 and 19.1 ± 5.7 dB, respectively. Similar MS (18.4 ± 7.6 dB) and RPS (20.0 ± 5.5 dB) values were found at baseline for Grid B. In Grid A, overall, MS, RPS and FCS progression rates were -0.57 ± 1.05, -0.74 ±1.24 and -1.26 ± 1.65 (all db/yr), respectively. FCS progression rate was significantly greater than the MS or RPS progression rates. Similar findings were observed in Grid B (MS -1.22 ± 1.42, RPS -1.44 ± 1.44, FCS -2.16 ± 2.24, all dB/yr), with paired t-test again demonstrated that FCS had a significantly faster rate of decline than MS or RPS. In patients with progression data in both grids, MS, RPS and FCS progression rates were significantly faster in the smaller Grid B. In 24 eyes with scotoma at baseline, fastest rate of decline was ESS combined with FCS compared to other progression parameters. CONCLUSIONSIncorporation of FCS can reduce confound of floor effect in perimetry analysis and can in turn detect a faster rate of decline.

Reference Database for a Novel Binocular Visual Function Perimeter: A Randomized Clinical Trial

PurposeTo construct a comprehensive reference database for a novel binocular automated perimeter. DesignA four-site prospective randomized clinical trial. Subjects and controls356 healthy subjects without ocular conditions that might affect visual function were categorized into 7 age groups. MethodsSubjects underwent comprehensive ocular examination of both eyes before enrollment. Using the TEMPO/IMOvifa automated perimeter (Topcon Healthcare/CREWT Medical Systems, Tokyo, Japan), each subject completed four binocular threshold visual field (VF) tests during a single visit: First, practice 24-2 and 10-2 tests were obtained from both eyes. Next, study 24-2 and 10-2 tests were obtained from both eyes. Test order of each sequence was randomized, and the tests were conducted under Standard Automated Perimetry testing conditions (SAP): Goldmann stimulus size III, 3183 cd/m2 maximum stimulus intensity, and background intensity of 10 cd/m2, using AIZE-Rapid test strategy. Standard VF reliability indices were assessed. For each subject, 24-2 and 10-2 test results from one randomly selected eye were analyzed. Main Outcome MeasuresPerimetric threshold sensitivity and reference limits for each test analysis parameter. ResultsThe ages of the study cohort were widely distributed, with a mean age (SD) of 52.3 (18.5) years. Sex assignment was 44.0% male and 56.0% female. The majority of subjects self-identified as White (67.4%), followed by Black or African American (13.5%) and Asian (8.7%), with 14.6% self-identified as Hispanic or Latino ethnicity. Mean sensitivity (MS) (SD) was 29.1 (1.3) decibels (dB) for the 24-2 and 32.4 (1.0) dB for the 10-2 test. For the 24-2 and 10-2, MS (SD) age-related changes averaged -0.06 (0.01) dB and -0.05 (0.01) dB per year, respectively. The normal range of pointwise threshold sensitivity increased with eccentricity and showed asymmetry around the mean, particularly notable in the 24-2 test. Mean (SD) binocular test duration was 3.18 (0.38) minutes (1 minute 35 seconds per eye) for the 24-2 test and 3.58 (0.43) minutes (1minute 47 seconds per eye) for the 10-2 test. ConclusionsA reference database for the TEMPO/IMOvifa perimeter was established, highlighting the significance of considering both age and stimulus eccentricity in interpreting threshold visual field test results.

Altitudinal Difference of Growth–Climate Response Models in the Coniferous Forests of Southeastern Tibetan Plateau, China

Characterized as a climatologically sensitive region, the southeastern Tibetan Plateau (STP) is an ideal location for dendrochronological research. Here, five tree-ring width (TRW) chronologies were developed: three for Picea likiangensis along altitudinal gradients from 3600 to 4400 m a.s.l. and two for Sabina saltuaria and Abies squamata from 4200 m a.s.l. Significant differences in the growth rates and age composition of Picea likiangensis were observed at various elevation gradients. The chronology statistics (mean sensitivity, etc.) fluctuated with the elevation gradient. Picea likiangensis showed distinct growth patterns in response to climatic variability along the altitude gradient: the minimum temperature influenced tree growth at lower and middle altitudes, while higher altitudes were affected by precipitation. The radial growth of different tree species growing in the same region is controlled by the same climatic factors. Sabina saltuaria and Abies squamata exhibited similar growth responses to Picea likiangensis. Stand conditions (wind speeds, slope, and elevation) and biotic factors (the depth of root, forest type, tree age, and sensitivity) can partially explain why the ring width–climate relationships change with altitude.

Continued harmonization of the international normalized ratio across a large laboratory network: Evidence of sustained low interlaboratory variation and bias after a change in instrumentation.

Our objective was to maintain low interlaboratory variation and bias in international normalized ratio (INR) results following a network change in instrumentation and reagents, using a process of ongoing standardization and harmonization. Network-wide standardization to new common instrument and reagent platforms followed by network-wide application of a simple novel process of verification of international sensitive index and mean normal prothrombin time values for each new lot of prothrombin time (PT) reagent that does not require use of World Health Organization reference thromboplastin or INR calibration/certified plasma. The network transitioned from mechanical hemostasis detection instruments with associated PT reagent (Diagnostica Stago; NeoPTimal) to optical detection (ACL TOPs) with associated PT reagent (Werfen; RecombiPlasTin 2G). Comparing 3 years of data for each situation, the network (n=27 laboratories) maintained low INR variability and bias relative to general mechanical and optical groups and other laboratories. Harmonized support for patient management of vitamin K antagonists such as warfarin was continuously maintained in our geography, with potentially positive implications for other coagulation laboratories and geographies. For the United States in particular, paucity of US Food and Drug Administration-cleared INR certified plasmas potentially compromises INR test accuracy; our novel approach may provide workable alternatives for other laboratories/networks.

Diagnostic Imaging Performance of Dual-Energy Computed Tomography Compared with Conventional Computed Tomography and Magnetic Resonance Imaging for Uterine Cervical Cancer.

Objective This article evaluates the ability of low-energy (40 keV) virtual monoenergetic images (VMIs) in the local diagnosis of cervical cancer compared with that of conventional computed tomography (C-CT) and magnetic resonance imaging (MRI), using clinicopathologic staging as a reference. Methods This prospective study included 33 patients with pathologically confirmed cervical cancer who underwent dual-energy CT and MRI between 2021 and 2022. The contrast-to-noise ratio (CNR) of the tumor-to-myometrium was compared between C-CT and VMI. Additionally, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) for each local diagnostic parameter were compared between C-CT, VMI, and MRI. Interradiologist agreement was also assessed. Results The mean CNR was significantly higher on VMI ( p = 0.002). No significant difference in AUC was found between C-CT and VMI for all local diagnostic parameters, and the specificity of VMI was often significantly less than that of MRI. For parametrial invasion, mean sensitivity, specificity, and AUC for C-CT, VMI, and MRI were 0.81, 0.99, 0.93; 0.64, 0.35, 0.79; and 0.73, 0.67, 0.86, respectively, and MRI had significantly higher specificity and AUC than that of VMI ( p = 0.013 and 0.008, respectively). Interradiologist agreement was higher for VMI than C-CT and for MRI than VMI. Conclusion The CNR of VMI was significantly higher than C-CT and interradiologist agreement was better than with C-CT; however, the overall diagnostic performance of VMI did not significantly differ from C-CT and was inferior to MRI. VMI was characterized by low specificity, which should be understood and used for reading.

Diagnostics Accuracy of Magnetic Resonance Imaging in Detection of Atherosclerotic Plaque Characteristics in Carotid Arteries Compared to Histology: A Systematic Review.

Carotid plaque composition represents one of the main risk factors of future ischemic stroke. MRI provides excellent soft tissue contrast that can distinguish plaque characteristics. Our objective was to analyze the diagnostic accuracy of MRI imaging in the detection of carotid plaque characteristics compared to histology in patients with symptomatic and asymptomatic carotid atherosclerosis through a systematic review. After prospective registration in PROSPERO (ID CRD42022329690), Medline Ovid, Embase.com, Cochrane Library, and Web of Science Core were searched without any search limitation up to May 27, 2022 to identify eligible articles. Of the 8168 studies, 53 (37 × 1.5 T MRI, 17 × 3 T MRI) evaluated MRI accuracy in the detection of 13 specific carotid plaque characteristics in 169 comparisons. MRI demonstrated high diagnostic accuracy for detection of calcification (3 T MRI: mean sensitivity 92%/mean specificity 90%; 1.5 T MRI: mean sensitivity 81%/mean specificity 91%), fibrous cap (1.5 T: 89%/87%), unstable plaque (1.5 T: 89%/87%), intraplaque hemorrhage (1.5 T: 86%/88%), and lipid-rich necrotic core (1.5 T: 89%/79%). MRI also proved to have a high level of tissue discrimination for the carotid plaque characteristics investigated, allowing potentially for a better risk assessment and follow-up of patients who may benefit from more aggressive treatments. These results emphasize the role of MRI as the first-line imaging modality for comprehensive assessment of carotid plaque morphology, particularly for unstable plaque. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 2.

Epileptic seizure forecasting with wearable-based nocturnal sleep features.

Non-invasive biomarkers have recently shown promise for seizure forecasting in people with epilepsy. In this work, we developed a seizure-day forecasting algorithm based on nocturnal sleep features acquired using a smart shirt. Seventy-eight individuals with epilepsy admitted to the Centre hospitalier de l'Université de Montréal epilepsy monitoring unit wore the Hexoskin biometric smart shirt during their stay. The shirt continuously measures electrocardiography, respiratory, and accelerometry activity. Ten sleep features, including sleep efficiency, sleep latency, sleep duration, time spent in non-rapid eye movement sleep (NREM) and rapid eye movement sleep (REM), wakefulness after sleep onset, average heart and breathing rates, high-frequency heart rate variability, and the number of position changes, were automatically computed using the Hexoskin sleep algorithm. Each night's features were then normalized using a reference night for each patient. A support vector machine classifier was trained for pseudo-prospective seizure-day forecasting, with forecasting horizons of 16- and 24-h to include both diurnal and nocturnal seizures (24-h) or diurnal seizures only (16-h). The algorithm's performance was assessed using a nested leave-one-patient-out cross-validation approach. Improvement over chance (IoC) performances were achieved for 48.7% and 40% of patients with the 16- and 24-h forecasting horizons, respectively. For patients with IoC performances, the proposed algorithm reached mean IoC, sensitivity and time in warning of 34.3%, 86.0%, and 51.7%, respectively for the 16-h horizon, and 34.2%, 64.4% and 30.2%, respectively, for the 24-h horizon. Smart shirt-based nocturnal sleep analysis holds promise as a non-invasive approach for seizure-day forecasting in a subset of people with epilepsy. Further investigations, particularly in a residential setting with long-term recordings, could pave the way for the development of innovative and practical seizure forecasting devices. Seizure forecasting with wearable devices may improve the quality of life of people living with epilepsy who experience unpredictable, recurrent seizures. In this study, we have developed a seizure forecasting algorithm using sleep characteristics obtained from a smart shirt worn at night by a large number of hospitalized patients with epilepsy (78). A daily seizure forecast was generated following each night using machine learning methods. Our results show that around half of people with epilepsy may benefit from such an approach.

Simplifying risk stratification for thyroid nodules on ultrasound: validation and performance of an artificial intelligence thyroid imaging reporting and data system

PurposeTo validate the performance of a recently created risk stratification system (RSS) for thyroid nodules on ultrasound, the Artificial Intelligence Thyroid Imaging Reporting and Data System (AI TI-RADS). Materials and methods378 thyroid nodules from 320 patients were included in this retrospective evaluation. All nodules had ultrasound images and had undergone fine needle aspiration (FNA). 147 nodules were Bethesda V or VI (suspicious or diagnostic for malignancy), and 231 were Bethesda II (benign). Three radiologists assigned features according to the AI TI-RADS lexicon (same categories and features as the American College of Radiology TI-RADS) to each nodule based on ultrasound images. FNA recommendations using AI TI-RADS and ACR TI-RADS were then compared and sensitivity and specificity for each RSS were calculated. ResultsAcross three readers, mean sensitivity of AI TI-RADS was lower than ACR TI-RADS (0.69 vs 0.72, p < 0.02), while mean specificity was higher (0.40 vs 0.37, p < 0.02). Overall total number of points assigned by all three readers decreased slightly when using AI TI-RADS (5,998 for AI TI-RADS vs 6,015 for ACR TI-RADS), including more values of 0 to several features. ConclusionAI TI-RADS performed similarly to ACR TI-RADS while eliminating point assignments for many features, allowing for simplification of future TI-RADS versions.

Integrating Radiomics and Neural Networks for Knee Osteoarthritis Incidence Prediction.

Accurately predicting knee osteoarthritis (KOA) is essential for early detection and personalized treatment. We aimed to develop and test a magnetic resonance imaging (MRI)-based joint space (JS) radiomic model (RM) to predict radiographic KOA incidence through neural networks by integrating meniscus and femorotibial cartilage radiomic features. In the Osteoarthritis Initiative cohort, participants with knees without radiographic KOA at baseline but at high risk for radiographic KOA were included. Patients' knees developed radiographic KOA, whereas control knees did not over four years. We randomly split the participants into development and test cohorts (8:2) and extracted features from baseline three-dimensional double-echo steady-state sequence MRI. Model performance was evaluated using an area under the receiver operating characteristic curve (AUC), sensitivity, and specificity in both cohorts. Nine resident surgeons performed the reader experiment without/with the JS-RM aid. Our study included 549 knees in the development cohort (275 knees of patients with KOA vs 274 knees of controls) and 137 knees in the test cohort (68 knees of patients with KOA vs 69 knees of controls). In the test cohort, JS-RM had a favorable accuracy for predicting the radiographic KOA incidence with an AUC of 0.931 (95% confidence interval [CI] 0.876-0.963), a sensitivity of 84.4% (95% CI 83.9%-84.9%), and a specificity of 85.6% (95% CI 85.2%-86.0%). The mean specificity and sensitivity of resident surgeons through MRI reading in predicting radiographic KOA incidence were increased from 0.474 (95% CI 0.333-0.614) and 0.586 (95% CI 0.429-0.743) without the assistance of JS-RM to 0.874 (95% CI 0.847-0.901) and 0.812 (95% CI 0.742-0.881) with JS-RM assistance, respectively (P < 0.001). JS-RM integrating the features of the meniscus and cartilage showed improved predictive values in radiographic KOA incidence.

Fuzzy random sensitivity analysis for the overall structure reliability of reinforced concrete freezing wellbores in deep alluvium based on hidden Markov model

To address the shortcomings of traditional reliability theory in characterizing the stability of deep underground structures, the advanced first order second moment of reliability was improved to obtain fuzzy random reliability, which is more consistent with the working conditions. The traditional sensitivity analysis model was optimized using fuzzy random optimization, and an analytical calculation model of the mean and standard deviation of the fuzzy random reliability sensitivity was established. A big data hidden Markov model and expectation-maximization algorithm were used to improve the digital characteristics of fuzzy random variables. The fuzzy random sensitivity optimization model was used to confirm the effect of concrete compressive strength, thick-diameter ratio, reinforcement ratio, uncertainty coefficient of calculation model, and soil depth on the overall structural reliability of a reinforced concrete double-layer wellbore in deep alluvial soil. Through numerical calculations, these characteristics were observed to be the main influencing factors. Furthermore, while the soil depth was negatively correlated, the other influencing factors were all positively correlated with the overall reliability. This study provides an effective reference for the safe construction of deep underground structures in the future.

Updates to the Physiologic Mechanism, Anatomical Sites, and Diagnostic Utility of the Scratch Collapse Test: A Systematic Review.

The scratch collapse test (SCT) has gained popularity as a physical examination technique for diagnosing compression neuropathy. This systematic review aims to assess the reliability of the SCT as a diagnostic tool for compression neuropathy, as well as to propose the underlying physiological mechanisms involved. Specific criteria was developed to broaden the potential anatomical applications of the SCT. A literature search was conducted using PubMed, Embase, Scopus, and Google Scholar. Eleven articles meeting predefined inclusion/exclusion criteria were selected for numerical analysis, which yielded sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy values. In total, 890 patients with carpal tunnel syndrome were reported in 10 studies. The mean (±SD) sensitivity, specificity, PPV, NPV, and accuracy were 0.442 ± 0.272, 0.788 ± 0.163, 0.834 ± 0.143, 0.433 ± 0.297, and 48.8% (range, 31%-82%), respectively. Of the studies that provided interrater reliability (κ), the mean was 0.544 ± 0.441, indicating moderate agreement. A total of 121 patients with cubital tunnel syndrome were reported in three studies, with a mean (±SD) sensitivity and specificity of 0.635 ± 0.367 and 0.945 ± 0.06, respectively. Twenty-four patients with peroneal nerve compression, reported in one study, had sensitivity, specificity, PPV, NPV, and accuracy of 0.77, 0.99, 0.95, 0.92, and 93%, respectively. Current literature indicates that the SCT can serve as a provocative test to assist in diagnosing compression neuropathy. Nevertheless, the heterogeneity of reported values underscores the necessity for further investigation aimed at enhancing the objectivity of SCT, thus improving interrater reliability and minimizing potential bias.

Determination of Mesalamine in Bulk and Suppository Dosage Forms through the Development and Validation of Stability- indicating RP-HPLC Method

Background: In the current study, a simple and cost-effective stability-indicating RP-HPLC method was developed and validated to estimate the mesalamine from both bulk and pharmaceutical dosage forms. Methods: An isocratic HPLC method using a reverse phase HiQSilC18 column (250 x 4.6 mm, 5μm) and a mobile phase methanol: ammonium acetate buffer (90:10 v/v) were employed as the mobile phase with a flow rate of 1 mL/min at 25°C. Detection was carried out at 305 nm, and the injection volume was 20μl. The developed method was validated as per ICH Q2 guidelines. Mesalamine has been subjected to various stress testing conditions, such as hydrolysis of acid and base, thermal degradation, oxidation, and photolysis. Also, methods have been validated with regard to linearity, accuracy, precision, and robustness. Results: The RT of mesalamine was determined to be 3.550 min ± 0.024 minutes, providing a reliable marker for its identification. The method was found to be linear between 5-30 μg/mL concentration with (R²) of 0.994. This demonstrated the method's ability to measure varying concentrations of mesalamine accurately. Additionally, the percentage recovery of mesalamine was approximately 100%, confirming the accuracy of the developed method. The parameters for system suitability have also been found to be within acceptable limits. Force degradation studies reinforced the method's selectivity and sensitivity in detecting mesalamine under various degradation scenarios. Notably, mesalamine significantly degraded in an acidic environment. Conclusion: In conclusion, our proposed RP-HPLC method provides a sensitive, accurate, and precise means of analyzing mesalamine in both bulk and pharmaceutical dosage forms.

Review of meta-analyses on the use of artificial intelligence in radiology

Introduction. Artificial intelligence (AI) is an effective tool for automating routine tasks in radiology. The diagnostic accuracy of AI in detecting various pathologies on medical images has generated considerable interest in the scientific community: the number of studies and meta-analyses has been constantly growing. The abundance of published evidence and the diversity of outcomes necessitate the need to systematize the available publications. The aim of this paper is to conduct an umbrella systematic review of contemporary meta-analyses on the use of AI in radiology.Materials and methods. PubMed was searched for studies published in the English language. Thirty-eight systematic reviews with meta-analyses published between 2021 and 2023 were selected for full-text analysis. The extracted data included the goal, study design, imaging modality, sample size, quality assessment of the included studies, AI diagnostic accuracy estimates, reference method parameters, and clinical efficacy metrics of AI implementation. The methodological quality of included systematic reviews was assessed using the AMSTAR-2 tool.Results. Nearly half (47%) of the included meta-analyses focused on the diagnosis, staging and segmentation of malignancies. Four meta-analyses were related to detection of maxillofacial structures in dentistry, while another four meta-analyses addressed the diagnosis of brain lesions. The diagnosis of COVID-19 and the diagnosis of bone fractures were each covered in three meta-analyses. One meta-analysis was reviewed for each of the following fields: colorectal polyps, pneumothorax, pulmonary embolism, osteoporosis, aneurysms, multiple sclerosis, acute cerebrovascular accident, intracranial hemorrhage, burns, and the risk of intrauterine growth restriction. Thirty-five (92%) meta-analyses assessed the risk of bias. Twenty-eight (80%) meta-analyses utilized QUADAS-2 to assess the risk of bias. 14 out of 28 papers reported low risk of bias (50%); 4 (14%) – moderate; 10 (36%) – high. The major risks were associated with samples that were unbalanced in terms of size and composition, a lack of details about the methods, a low number of prospective studies, and a lack of external validation of the outcomes. The overall results indicate that the diagnostic accuracy of AI is comparable to or even greater than that of radiologists. The mean sensitivity, specificity and area under the ROC curve for AI and radiologists were 85.2%, 89.5%, 93.5% and 84.4%, 90.0%, 92.8%, respectively. However, many studies that compared the diagnostic accuracy of AI and radiologists lack the data on the number and experience of the latter. Only one paper presented results of implementing AI into routine clinical diagnosis.Discussion. AI is capable of reducing the turnaround time for non-urgent examinations. When used to verify the primary interpretation, AI was effective in detecting false-negative results from radiologists. However, the efficacy of detecting false-positive results was inadequate. Our assessment of the quality of systematic reviews with AMSTAR-2 show that the methods of searching, selecting and analyzing literature must be improved and brought to a common standard. The development of a specialized tool for assessing the quality of systematic reviews in the AI implementation is also necessary. Due to high diagnostic accuracy, AI is currently considered a promising tool for optimizing the turnaround time. However, more evidence is needed to study the AI outcomes in routine clinical practice. Furthermore, it is necessary to standardize and improve the quality of research methodology.

An intelligent data-driven investigation and optimization integrated with an eco-friendly thermal design approach for a marine diesel engine to study its waste-to-liquefied hydrogen generation potential

The waste heat generated by vehicles, especially heavy-duty engines, has motivated the current research to propose a novel thermal design for a ship's engine. Hence, the suggested system comprises an organic-flash power plant, a bi-evaporator cooling subsystem, a thermal desalination cycle, a water electrolysis-based hydrogen generation unit, and a Claude unit for liquefying hydrogen. The innovative aspects associated with the proposed design include utilizing a novel cascade heat recovery process aimed at reduced irreversibility for the engine's waste and the integration of bi-evaporator technology and Claude unit to facilitate the process of waste-to-liquefied hydrogen generation for a marine engine for improved handling. In addition, the research includes an intelligent study that comprehensively analyzes and optimizes the proposed system in terms of long-term sustainability and economic feasibility. For this purpose, a data-driven optimization technique is implemented using artificial neural networks in combination with multi-objective grey wolf optimization. The aims of the research encompass the optimization of the sustainability index and unit cost of liquefied hydrogen. The research findings indicate that the vapor generator’s terminal temperature difference exerts the most substantial influence on performance metrics, as evidenced by a mean sensitivity index of 0.368. Also, the mentioned objective functions exhibit values of 1.139 and 7.60 $/kg, respectively. Besides, the optimum exergy destruction rate is 124.3 kW, the total investment cost rate is 4.94 $/year, the specific cost of products is 74.87 /GJ, the payback period is 2.2 years, and the net present value is 7.48 M$.

Automatic detection of microaneurysms using DeTraC deep convolutional neural network classifier with woodpecker mating algorithm

ABSTRACT Diabetic Retinopathy (DR) is a microvascular complication of diabetes that leads to visual blindness. Early identification of DR can prevent the loss of sight. The first visible sign of DR is the appearance of micro aneurysms (MAs). Micro aneurysms are seen as small red circular spots on the retinal surface. The very small size of micro aneurysms proves to be challenging in its proper detection. In this research, DeTrac Deep Convolutional Neural Network m classifier with Woodpecker Mating Algorithm is proposed for the detection of MAs. By using this technique, every image is classified as either MAs or non-MAs pixel to automatically detect micro aneurysms from the retinal images. Experimental results are evaluated on diabetic-retinopathy-detection (DRD) dataset of the Kaggle website. Extensive simulations on this dataset shows an improved performance over the existing methods with 0.98 mean sensitivity, 0.97 mean specificity, and 0.98 mean accuracy in detecting the MAs irrespective of their intrinsic properties.

Fragment distance-guided dual-stream learning for automatic pelvic fracture segmentation

Pelvic fracture is a complex and severe injury. Accurate diagnosis and treatment planning require the segmentation of the pelvic structure and the fractured fragments from preoperative CT scans. However, this segmentation is a challenging task, as the fragments from a pelvic fracture typically exhibit considerable variability and irregularity in the morphologies, locations, and quantities. In this study, we propose a novel dual-stream learning framework for the automatic segmentation and category labeling of pelvic fractures. Our method uniquely identifies pelvic fracture fragments in various quantities and locations using a dual-branch architecture that leverages distance learning from bone fragments. Moreover, we develop a multi-size feature fusion module that adaptively aggregates features from diverse receptive fields tailored to targets of different sizes and shapes, thus boosting segmentation performance. Extensive experiments on three pelvic fracture datasets from different medical centers demonstrated the accuracy and generalizability of the proposed method. It achieves a mean Dice coefficient and mean Sensitivity of 0.935±0.068 and 0.929±0.058 in the dataset FracCLINIC, and 0.955±0.072 and 0.912±0.125 in the dataset FracSegData, which are superior than other comparing methods. Our method optimizes the process of pelvic fracture segmentation, potentially serving as an effective tool for preoperative planning in the clinical management of pelvic fractures.

Impact of QRS misclassifications on heart-rate-variability parameters (results from the CARLA cohort study).

Heart rate variability (HRV), an important marker of autonomic nervous system activity, is usually determined from electrocardiogram (ECG) recordings corrected for extrasystoles and artifacts. Especially in large population-based studies, computer-based algorithms are used to determine RR intervals. The Modular ECG Analysis System MEANS is a widely used tool, especially in large studies. The aim of this study was therefore to evaluate MEANS for its ability to detect non-sinus ECG beats and artifacts and to compare HRV parameters in relation to ECG processing. Additionally, we analyzed how ECG processing affects the statistical association of HRV with cardiovascular disease (CVD) risk factors. 20-min ECGs from 1,674 subjects of the population-based CARLA study were available for HRV analysis. All ECGs were processed with the ECG computer program MEANS. A reference standard was established by experienced clinicians who visually inspected the MEANS-processed ECGs and reclassified beats if necessary. HRV parameters were calculated for 5-minute segments selected from the original 20-minute ECG. The effects of misclassified typified normal beats on i) HRV calculation and ii) the associations of CVD risk factors (sex, age, diabetes, myocardial infarction) with HRV were modeled using linear regression. Compared to the reference standard, MEANS correctly classified 99% of all beats. The averaged sensitivity of MEANS across all ECGs to detect non-sinus beats was 76% [95% CI: 74.1;78.5], but for supraventricular extrasystoles detection sensitivity dropped to 38% [95% CI: 36.8;38.5]. Time-domain parameters were less affected by false sinus beats than frequency parameters. Compared to the reference standard, MEANS resulted in a higher SDNN on average (mean absolute difference 1.4ms [95% CI: 1.0;1.7], relative 4.9%). Other HRV parameters were also overestimated as well (between 6.5 and 29%). The effect estimates for the association of CVD risk factors with HRV did not differ between the editing methods. We have shown that the use of the automated MEANS algorithm may lead to an overestimation of HRV due to the misclassification of non-sinus beats, especially in frequency domain parameters. However, in population-based studies, this has no effect on the observed associations of HRV with risk factors, and therefore an automated ECG analyzing algorithm as MEANS can be recommended here for the determination of HRV parameters.