Significant controversies surround the optimal treatment of primary hyperhidrosis of the hands, axillae, feet, and face. The world's literature on hyperhidrosis from 1991 to 2009 was obtained through ...PubMed. There were 1,097 published articles, of which 102 were clinical trials. Twelve were randomized clinical trials and 90 were nonrandomized comparative studies. After review and discussion by task force members of The Society of Thoracic Surgeons' General Thoracic Workforce, expert consensus was reached from which specific treatment strategies are suggested. These studies suggest that primary hyperhidrosis of the extremities, axillae or face is best treated by endoscopic thoracic sympathectomy (ETS). Interruption of the sympathetic chain can be achieved either by electrocautery or clipping. An international nomenclature should be adopted that refers to the rib levels (R) instead of the vertebral level at which the nerve is interrupted, and how the chain is interrupted, along with systematic pre and postoperative assessments of sweating pattern, intensity and quality-of-life. The recent body of literature suggests that the highest success rates occur when interruption is performed at the top of R3 or the top of R4 for palmar-only hyperhidrosis. R4 may offer a lower incidence of compensatory hyperhidrosis but moister hands. For palmar and axillary, palmar, axillary and pedal and for axillary-only hyperhidrosis interruptions at R4 and R5 are recommended. The top of R3 is best for craniofacial hyperhidrosis.
The hematoxylin and eosin (H&E) stain is the standard used for microscopic examination of tissues that have been fixed, processed, embedded, and sectioned. It can be performed manually or by ...automation. For economic reasons, the manual technique is generally the method of choice for facilities with a low sample volume. This protocol describes manual H&E staining of fixed, processed, paraffin-embedded, and sectioned mouse tissues. In H&E-stained tissues, the nucleic acids stain dark blue and the proteins stain red to pink or orange. For accurate phenotyping and delineation of tissue detail, the protocol must be adhered to rigorously. This includes frequent reagent changes as well as the use of "in-date" reagents. Appropriate color in a good H&E stain allows for identification of many tissue subtleties that are necessary for accurate diagnosis.
Explainable artificial intelligence (AI) can be integrated within standard clinical software to facilitate the acceptance of the diagnostic findings during clinical interpretation.
This study sought ...to develop and evaluate a novel, general purpose, explainable deep learning model (coronary artery disease–deep learning CAD-DL) for the detection of obstructive CAD following single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI).
A total of 3,578 patients with suspected CAD undergoing SPECT MPI and invasive coronary angiography within a 6-month interval from 9 centers were studied. CAD-DL computes the probability of obstructive CAD from stress myocardial perfusion, wall motion, and wall thickening maps, as well as left ventricular volumes, age, and sex. Myocardial regions contributing to the CAD-DL prediction are highlighted to explain the findings to the physician. A clinical prototype was integrated using a standard clinical workstation. Diagnostic performance by CAD-DL was compared to automated quantitative total perfusion deficit (TPD) and reader diagnosis.
In total, 2,247 patients (63%) had obstructive CAD. In 10-fold repeated testing, the area under the receiver-operating characteristic curve (AUC) (95% CI) was higher according to CAD-DL (AUC: 0.83 95% CI: 0.82-0.85) than stress TPD (AUC: 0.78 95% CI: 0.77-0.80) or reader diagnosis (AUC: 0.71 95% CI: 0.69-0.72; P < 0.0001 for both). In external testing, the AUC in 555 patients was higher according to CAD-DL (AUC: 0.80 95% CI: 0.76-0.84) than stress TPD (AUC: 0.73 95% CI: 0.69-0.77) or reader diagnosis (AUC: 0.65 95% CI: 0.61-0.69; P < 0.001 for all). The present model can be integrated within standard clinical software and generates results rapidly (<12 seconds on a standard clinical workstation) and therefore could readily be incorporated into a typical clinical workflow.
The deep-learning model significantly surpasses the diagnostic accuracy of standard quantitative analysis and clinical visual reading for MPI. Explainable artificial intelligence can be integrated within standard clinical software to facilitate acceptance of artificial intelligence diagnosis of CAD following MPI.
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Senescence is the consequence of a signaling mechanism activated in stressed cells to prevent proliferation of cells with damage. Senescent cells (Sncs) often develop a senescence-associated ...secretory phenotype to prompt immune clearance, which drives chronic sterile inflammation and plays a causal role in aging and age-related diseases. Sncs accumulate with age and at anatomical sites of disease. Thus, they are regarded as a logical therapeutic target. Senotherapeutics are a new class of drugs that selectively kill Sncs (senolytics) or suppress their disease-causing phenotypes (senomorphics senostatics). Since 2015, several senolytics went from identification to clinical trial. Preclinical data indicate that senolytics alleviate disease in numerous organs, improve physical function and resilience, and suppress all causes of mortality, even if administered to the aged. Here, we review the evidence that Sncs drive aging and disease, the approaches to identify and optimize senotherapeutics, and the current status of preclinical and clinical testing of senolytics.
Optimal risk stratification with machine learning (ML) from myocardial perfusion imaging (MPI) includes both clinical and imaging data. While most imaging variables can be derived automatically, ...clinical variables require manual collection, which is time-consuming and prone to error. We determined the fewest manually input and imaging variables required to maintain the prognostic accuracy for major adverse cardiac events (MACE) in patients undergoing a single-photon emission computed tomography (SPECT) MPI.
This study included 20 414 patients from the multicentre REFINE SPECT registry and 2984 from the University of Calgary for training and external testing of the ML models, respectively. ML models were trained using all variables (ML-All) and all image-derived variables (including age and sex, ML-Image). Next, ML models were sequentially trained by incrementally adding manually input and imaging variables to baseline ML models based on their importance ranking. The fewest variables were determined as the ML models (ML-Reduced, ML-Minimum, and ML-Image-Reduced) that achieved comparable prognostic performance to ML-All and ML-Image. Prognostic accuracy of the ML models was compared with visual diagnosis, stress total perfusion deficit (TPD), and traditional multivariable models using area under the receiver-operating characteristic curve (AUC). ML-Minimum (AUC 0.798) obtained comparable prognostic accuracy to ML-All (AUC 0.799, P = 0.19) by including 12 of 40 manually input variables and 11 of 58 imaging variables. ML-Reduced achieved comparable accuracy (AUC 0.796) with a reduced set of manually input variables and all imaging variables. In external validation, the ML models also obtained comparable or higher prognostic accuracy than traditional multivariable models.
Reduced ML models, including a minimum set of manually collected or imaging variables, achieved slightly lower accuracy compared to a full ML model but outperformed standard interpretation methods and risk models. ML models with fewer collected variables may be more practical for clinical implementation.
Stress-only myocardial perfusion imaging (MPI) markedly reduces radiation dose, scanning time, and cost. We developed an automated clinical algorithm to safely cancel unnecessary rest imaging with ...high sensitivity for obstructive coronary artery disease (CAD).
Patients without known CAD undergoing both MPI and invasive coronary angiography from REFINE SPECT were studied. A machine learning score (MLS) for prediction of obstructive CAD was generated using stress-only MPI and pre-test clinical variables. An MLS threshold with a pre-defined sensitivity of 95% was applied to the automated patient selection algorithm. Obstructive CAD was present in 1309/2079 (63%) patients. MLS had higher area under the receiver operator characteristic curve (AUC) for prediction of CAD than reader diagnosis and TPD (0.84 vs 0.70 vs 0.78, P < .01). An MLS threshold of 0.29 had superior sensitivity than reader diagnosis and TPD for obstructive CAD (95% vs 87% vs 87%, P < .01) and high-risk CAD, defined as stenosis of the left main, proximal left anterior descending, or triple-vessel CAD (sensitivity 96% vs 89% vs 90%, P < .01).
The MLS is highly sensitive for prediction of both obstructive and high-risk CAD from stress-only MPI and can be applied to a stress-first protocol for automatic cancellation of unnecessary rest imaging.
Purpose
Artificial intelligence (AI) has high diagnostic accuracy for coronary artery disease (CAD) from myocardial perfusion imaging (MPI). However, when trained using high-risk populations (such as ...patients with correlating invasive testing), the disease probability can be overestimated due to selection bias. We evaluated different strategies for training AI models to improve the calibration (accurate estimate of disease probability), using external testing.
Methods
Deep learning was trained using 828 patients from 3 sites, with MPI and invasive angiography within 6 months. Perfusion was assessed using upright (U-TPD) and supine total perfusion deficit (S-TPD). AI training without data augmentation (model 1) was compared to training with augmentation (increased sampling) of patients without obstructive CAD (model 2), and patients without CAD and TPD < 2% (model 3). All models were tested in an external population of patients with invasive angiography within 6 months (
n
= 332) or low likelihood of CAD (
n
= 179).
Results
Model 3 achieved the best calibration (Brier score 0.104 vs 0.121,
p
< 0.01). Improvement in calibration was particularly evident in women (Brier score 0.084 vs 0.124,
p
< 0.01). In external testing (
n
= 511), the area under the receiver operating characteristic curve (AUC) was higher for model 3 (0.930), compared to U-TPD (AUC 0.897) and S-TPD (AUC 0.900,
p
< 0.01 for both).
Conclusion
Training AI models with augmentation of low-risk patients can improve calibration of AI models developed to identify patients with CAD, allowing more accurate assignment of disease probability. This is particularly important in lower-risk populations and in women, where overestimation of disease probability could significantly influence down-stream patient management.
Nanoparticulate titanium dioxide (TiO(2)) is highly photoactive, and its function as a photocatalyst drives much of the application demand for TiO(2). Because TiO(2) generates reactive oxygen species ...(ROS) when exposed to ultraviolet radiation (UVR), nanoparticulate TiO(2) has been used in antibacterial coatings and wastewater disinfection, and has been investigated as an anti-cancer agent. Oxidative stress mediated by photoactive TiO(2) is the likely mechanism of its toxicity, and experiments demonstrating cytotoxicity of TiO(2) have used exposure to strong artificial sources of ultraviolet radiation (UVR). In vivo tests of TiO(2) toxicity with aquatic organisms have typically shown low toxicity, and results across studies have been variable. No work has demonstrated that photoactivity causes environmental toxicity of TiO(2) under natural levels of UVR. Here we show that relatively low levels of ultraviolet light, consistent with those found in nature, can induce toxicity of TiO(2) nanoparticles to marine phytoplankton, the most important primary producers on Earth. No effect of TiO(2) on phytoplankton was found in treatments where UV light was blocked. Under low intensity UVR, ROS in seawater increased with increasing nano-TiO(2) concentration. These increases may lead to increased overall oxidative stress in seawater contaminated by TiO(2), and cause decreased resiliency of marine ecosystems. Phototoxicity must be considered when evaluating environmental impacts of nanomaterials, many of which are photoactive.
99mTc-pyrophosphate imaging has emerged as an important non-invasive method to diagnose transthyretin cardiac amyloidosis (ATTR-CM). Quantitation of 99mTc-pyrophosphate activity, on SPECT images, ...could be a marker of ATTR-CM disease burden. We assessed the diagnostic accuracy and clinical significance of 99mTc-pyrophosphate quantitation.
Patients who underwent 99mTc-pyrophosphate imaging for suspected ATTR-CM were included. Using SPECT images, radiotracer activity in the myocardium was calculated using cardiac pyrophosphate activity (CPA) and volume of involvement (VOI), with thresholds for abnormal activity derived from LVBP activity. Diagnostic accuracy was assessed using area under the receiver operating characteristic curve (AUC). In total, 124 patients were identified, mean age 73.9 ± 11.4, with ATTR-CM diagnosed in 43 (34.7%) patients. CPA had the highest diagnostic accuracy (AUC .996, 95% CI .987-1.00), and was significantly higher compared to the Perugini score (AUC .952, P = .016). In patients with ATTR-CM, CPA was associated with reduced left ventricular ejection fraction (adjusted odds ratio 1.28, P = .035) and heart failure hospitalizations (adjusted hazard ratio 1.29, P = .006).
Quantitative assessment of myocardial radiotracer activity with CPA or VOI have high diagnostic accuracy for ATTR-CM. Both measures are potential non-invasive markers to follow progression of disease or response to therapy.
Las imágenes de 99mTc-pirofosfato han surgido como un importante método no invasivo para diagnosticar la amiloidosis cardíaca por transtiretina (ATTR-CM). La cuantificación de la actividad del 99mTc-pirofosfato, en las imágenes de SPECT, podría ser un marcador de la carga de enfermedad de ATTR-CM. Evaluamos la precisión diagnóstica y la importancia clínica de la cuantificación del 99mTc-pirofosfato.
Se incluyeron pacientes que se sometieron 99mTc-pirofosfato por sospecha de ATTR-CM. Utilizando imágenes de SPECT, se calculó la actividad del radiotrazador en el miocardio utilizando la actividad cardiaca del pirofosfato (CPA) y el volumen del involucro (VOI), con umbrales para la actividad anormal derivados de la actividad del LVBP. La precisión diagnóstica se evaluó utilizando el área bajo la curva (AUC) de las características del funcionamiento del receptor. En total, se identificaron 124 pacientes, edad media 73,9 ± 11,4, con ATTR-CM diagnosticado en 43 (34,7%) pacientes. La CPA tuvo la mayor certeza diagnóstica (AUC 0.996, IC del 95% 0.987 – 1.00), y fue significativamente mayor en comparación con el score de Perugini (AUC 0.952, p = 0.016). En pacientes con ATTR-CM, la CPA se asoció con una fracción de eyección del ventrículo izquierdo disminuida (odds ratio ajustada 1.28, p = 0.035) y hospitalizaciones por falla cardíaca (hazard ratio ajustado 1.29, p =0,006).
La evaluación cuantitativa de la actividad del radiotrazador en miocárdico con CPA o VOI tiene una alta certeza diagnóstica para ATTR-CM. Ambas medidas son potenciales marcadores no invasivos para seguir la progresión de la enfermedad o la respuesta a terapia.
99m锝-焦磷酸盐成像已成为诊断运甲状腺素蛋白心脏淀粉样变性(ATTR-CM)的一种重要的非侵入性方法。 在SPECT图像上定量分析99m锝-焦磷酸盐的活性可能是ATTR-CM疾病严重程度的标志。 本文评估了99m锝-焦磷酸盐定量分析的诊断准确性和临床意义。
接受99m锝-焦磷酸盐成像的疑似ATTR-CM的患者纳入本研究。在SPECT图像中,使用心脏焦磷酸盐活性(CPA)和受累体积(VOI)计算心肌中的放射性示踪剂活性,并根据LVBP活性得出异常活性的阈值。 使用接受者操作特征曲线下的面积(AUC)评估诊断准确性。本实验共入组124例患者,平均年龄为73±11.4,其中43例(34.7%)患者被诊断为ATTR-CM。 CPA的诊断准确度最高(AUC 0.996,95%CI 0.987 – 1.00),与Perugini评分相比明显更高(AUC 0.952,p = 0.016)。 在ATTR-CM的患者中,CPA与左心室射血分数降低(调整后的优势比1.28,p = 0.035)和心衰住院(调整后的危险比1.29,p = 0.006)相关。
使用CPA或VOI定量评估心肌放射性示踪剂的活性对ATTR-CM具有很高的诊断准确性。 两种方法都是潜在的非侵入性指标,可跟踪疾病的进展或治疗效果。
L’imagerie au 99mTc-pyrophosphate est devenue une méthode non invasive importante pour le diagnostic de l’amyloidose cardiaque à transthyrétine (ATTR-CM). La quantification de l’activité du 99mTc-pyrophosphate sur les images SPECT, pourrait être un marqueur de l’intensité de la maladie. Dans cette étude, nous avons évalué la précision diagnostique et la signification clinique de la quantification au 99mTc-pyrophosphate.
Les patients ayant bénéficié d’une imagerie au 99mTc-pyrophosphate pour suspicion d’ATTR-CM ont été inclus. Nous avons calculé l’activité (CPA) et le volume (VOI) du 99mTc-pyrophosphate (CPA) au niveau du myocarde sur les images SPECT en rapport à l’activité sanguine au niveau de la cavité ventriculaire gauche. La précision du test a été évaluée en utilisant la surface (AUC) sous la courbe ROC. Au total, 124 patients ont été étudiés (âge moyen de 73,9 ± 11,4). Quarante trois de ces patients (34.7%) furent diagnostiqués positivement pour l’ ATTR-CM. La précision diagnostique de la CPA s’est révélée la plus élevée (AUC 0,996, IC à 95% 0,987 - 1,00), et s’est avérée significativement plus élevée que le score de Perugini (AUC 0,952, p = 0,016). Chez les patients avec ATTR-CM, la CPA est associée à une fraction d’éjection ventriculaire gauche réduite (odds ratio ajusté de 1,28, p = 0,035) et une augmentation d’ hospitalisation pour insuffisance cardiaque (hazard ratio ajusté de 1,29, p = 0,006).
L’évaluation quantitative de l’activité du 99mTc-pyrophosphate est d’une grande précision diagnostique pour l’ ATTR-CM. Les mesures CAP et VOI sont des marqueurs non invasifs potentiels pour le suivi de la progression de la maladie ou réponse au traitement.
Artificial intelligence may improve accuracy of myocardial perfusion imaging (MPI) but will likely be implemented as an aid to physician interpretation rather than an autonomous tool. Deep learning ...(DL) has high standalone diagnostic accuracy for obstructive coronary artery disease (CAD), but its influence on physician interpretation is unknown. We assessed whether access to explainable DL predictions improves physician interpretation of MPI.
We selected a representative cohort of patients who underwent MPI with reference invasive coronary angiography. Obstructive CAD, defined as stenosis ≥50% in the left main artery or ≥70% in other coronary segments, was present in half of the patients. We used an explainable DL model (CAD-DL), which was previously developed in a separate population from different sites. Three physicians interpreted studies first with clinical history, stress, and quantitative perfusion, then with all the data plus the DL results. Diagnostic accuracy was assessed using area under the receiver-operating-characteristic curve (AUC).
In total, 240 patients with a median age of 65 y (interquartile range 58-73) were included. The diagnostic accuracy of physician interpretation with CAD-DL (AUC 0.779) was significantly higher than that of physician interpretation without CAD-DL (AUC 0.747,
= 0.003) and stress total perfusion deficit (AUC 0.718,
< 0.001). With matched specificity, CAD-DL had higher sensitivity when operating autonomously compared with readers without DL results (
< 0.001), but not compared with readers interpreting with DL results (
= 0.122). All readers had numerically higher accuracy with CAD-DL, with AUC improvement 0.02-0.05, and interpretation with DL resulted in overall net reclassification improvement of 17.2% (95% CI 9.2%-24.4%,
< 0.001).
Explainable DL predictions lead to meaningful improvements in physician interpretation; however, the improvement varied across the readers, reflecting the acceptance of this new technology. This technique could be implemented as an aid to physician diagnosis, improving the diagnostic accuracy of MPI.
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