Assessing the Correlation between Allostatic Load and False-Positive Image-Guided Breast Biopsies.

Background: Allostatic load (AL) is the accumulation of physiological dysregulation attributed to repeated activation of the stress response over a lifetime. We assessed the utility of AL as a prognostic measure for high-risk benign breast biopsy pathology results. Method: Eligible patients were women 18 years or older, with a false-positive outpatient breast biopsy between January and December 2022 at a tertiary academic health center. AL was calculated using 12 variables representing four physiological systems: cardiovascular (pulse rate, systolic and diastolic blood pressures, total cholesterol, high-density lipoprotein, and low-density lipoprotein); metabolic (body mass index, albumin, and hemoglobin A1C); renal (creatinine and estimated glomerular filtration rate); and immune (white blood cell count). Multivariable logistic regression was used to assess the association between AL before biopsy and breast biopsy outcomes controlling for patients' sociodemographics. Results: In total, 170 women were included (mean age, 54.1 ± 12.9 years): 89.4% had benign and 10.6% had high-risk pathologies (radial scar/complex sclerosing lesion, atypical ductal or lobular hyperplasia, flat epithelial atypia, intraductal papilloma, or lobular carcinoma in-situ). A total of 56.5% were White, 24.7% Asian, and 17.1% other races. A total of 32.5% identified as Hispanic. The mean breast cancer risk score using the Tyrer-Cuzick model was 11.9 ± 7.0. In multivariable analysis, with every one unit increase in AL, the probability of high-risk pathology increased by 37% (odds ratio, 1.37; 95% confidence interval, 1.03, 1.81; p = 0.03). No significant association was seen between high-risk pathology and age, ethnicity, breast cancer risk, or area deprivation index. Conclusion: Our findings support that increased AL, a biological marker of stress, is associated with high-risk pathology among patients with false-positive breast biopsy results.

Physician marriage survey reveals sex and specialty differences in marital satisfaction factors.

Physician marriage is a valuable indicator of how vocational factors (e.g. work hours, stressors) impact satisfaction in relationships and physician wellness overall. Previous studies suggest that gender and specialty influence marriage satisfaction for physicians, though these often come from limited, local, cohorts. A cross-sectional survey was designed and distributed to publicly available email addresses representing academic and private practice physician organizations across the United States, receiving 321 responses (253 complete). Responses included data on demographics, medical specialty, age at marriage, stage of training at marriage, number of children, and factors leading to marital satisfaction/distress. A multivariable ordinal logistic regression was conducted to find associations between survey variables and marriage satisfaction. Survey results indicated that 86.5% of physicians have been married (average age at first marriage was 27.8 years old), and the rate of first marriages ending is at least 14.7%. Men had significantly more children than women. Physicians married at least once averaged 1.98 children. "Other" specialty physicians had significantly more children on average than psychiatrists. Marrying before medical school predicted practicing in private practice settings. Job stress, work hours, children, and sex were most frequently sources of marital distress, while strong communication, finances, and children were most frequently sources of marital stability. Sex differences were also found in distressing and stabilizing marital factors: Female physicians were more likely to cite their spouse's work hours and job stress as sources of marital distress. Finally, surgery specialty and Judaism were associated with higher marriage satisfaction, whereas possession of an M.D. degree was associated with lower marriage satisfaction. This study elucidated new perspectives on physician marriage and families based on specialty, practice setting, and stage of training at marriage. Future studies may focus on factors mediating specialty and sex's impact on having children and marriage satisfaction. To our knowledge, this study is the first physician marriage survey which integrates multiple factors in the analysis of physician marriages.

Chemogenetic Attenuation of Acute Nociceptive Signaling Enhances Functional Outcomes Following Spinal Cord Injury.

Identifying novel therapeutic approaches to promote recovery of neurological functions following spinal cord injury (SCI) remains a great unmet need. Nociceptive signaling in the acute phase of SCI has been shown to inhibit recovery of locomotor function and promote the development of chronic neuropathic pain. We therefore hypothesized that inhibition of nociceptive signaling in the acute phase of SCI might improve long-term functional outcomes in the chronic phase of injury. To test this hypothesis, we took advantage of a selective strategy utilizing AAV6 to deliver inhibitory (hM4Di) Designer Receptors Exclusively Activated by Designer Drugs (DREADDs) to nociceptors of the L4-L6 dorsal root ganglia to evaluate the effects of transient nociceptor silencing on long-term sensory and motor functional outcomes in a rat thoracic contusion SCI model. Following hM4Di-mediated nociceptor inhibition from 0-14 days post-SCI, we conducted behavioral assessments until 70 days post-SCI, then performed histological assessments of lesion severity and axon plasticity. Our results show highly selective expression of hM4Di within small diameter nociceptors including calcitonin gene-related peptide (CGRP)+ and IB4-binding neurons. Expression of hM4Di in less than 25% of nociceptors was sufficient to increase hindlimb thermal withdrawal latency in naïve rats. Compared with subjects who received AAV-yellow fluorescent protein (YFP; control), subjects who received AAV-hM4Di exhibited attenuated thermal hyperalgesia, greater coordination, and improved hindlimb locomotor function. However, treatment did not impact the development of cold allodynia or mechanical hyperalgesia. Histological assessments of spinal cord tissue suggested trends toward reduced lesion volume, increased neuronal sparing and increased CGRP+ axon sprouting in hM4Di-treated animals. Together, these findings suggest that nociceptor silencing early after SCI may promote beneficial plasticity in the acute phase of injury that can impact long-term functional outcomes, and support previous work highlighting primary nociceptors as possible therapeutic targets for pain management after SCI.

A principal odor map unifies diverse tasks in olfactory perception.

Mapping molecular structure to odor perception is a key challenge in olfaction. We used graph neural networks to generate a principal odor map (POM) that preserves perceptual relationships and enables odor quality prediction for previously uncharacterized odorants. The model was as reliable as a human in describing odor quality: On a prospective validation set of 400 out-of-sample odorants, the model-generated odor profile more closely matched the trained panel mean than did the median panelist. By applying simple, interpretable, theoretically rooted transformations, the POM outperformed chemoinformatic models on several other odor prediction tasks, indicating that the POM successfully encoded a generalized map of structure-odor relationships. This approach broadly enables odor prediction and paves the way toward digitizing odors.

SCOUR: a stepwise machine learning framework for predicting metabolite-dependent regulatory interactions.

BACKGROUND: The topology of metabolic networks is both well-studied and remarkably well-conserved across many species. The regulation of these networks, however, is much more poorly characterized, though it is known to be divergent across organisms-two characteristics that make it difficult to model metabolic networks accurately. While many computational methods have been built to unravel transcriptional regulation, there have been few approaches developed for systems-scale analysis and study of metabolic regulation. Here, we present a stepwise machine learning framework that applies established algorithms to identify regulatory interactions in metabolic systems based on metabolic data: stepwise classification of unknown regulation, or SCOUR. RESULTS: We evaluated our framework on both noiseless and noisy data, using several models of varying sizes and topologies to show that our approach is generalizable. We found that, when testing on data under the most realistic conditions (low sampling frequency and high noise), SCOUR could identify reaction fluxes controlled only by the concentration of a single metabolite (its primary substrate) with high accuracy. The positive predictive value (PPV) for identifying reactions controlled by the concentration of two metabolites ranged from 32 to 88% for noiseless data, 9.2 to 49% for either low sampling frequency/low noise or high sampling frequency/high noise data, and 6.6-27% for low sampling frequency/high noise data, with results typically sufficiently high for lab validation to be a practical endeavor. While the PPVs for reactions controlled by three metabolites were lower, they were still in most cases significantly better than random classification. CONCLUSIONS: SCOUR uses a novel approach to synthetically generate the training data needed to identify regulators of reaction fluxes in a given metabolic system, enabling metabolomics and fluxomics data to be leveraged for regulatory structure inference. By identifying and triaging the most likely candidate regulatory interactions, SCOUR can drastically reduce the amount of time needed to identify and experimentally validate metabolic regulatory interactions. As high-throughput experimental methods for testing these interactions are further developed, SCOUR will provide critical impact in the development of predictive metabolic models in new organisms and pathways.