Category: Nevin Manimala

J Clin Oncol. 2023 Dec 11:JCO2301101. doi: 10.1200/JCO.23.01101. Online ahead of print.

ABSTRACT

PURPOSE: There is strong evidence that leisure-time physical activity is protective against postmenopausal breast cancer risk but the association with premenopausal breast cancer is less clear. The purpose of this study was to examine the association of physical activity with the risk of developing premenopausal breast cancer.

METHODS: We pooled individual-level data on self-reported leisure-time physical activity across 19 cohort studies comprising 547,601 premenopausal women, with 10,231 incident cases of breast cancer. Multivariable Cox regression was used to estimate hazard ratios (HRs) and 95% CIs for associations of leisure-time physical activity with breast cancer incidence. HRs for high versus low levels of activity were based on a comparison of risk at the 90th versus 10th percentiles of activity. We assessed the linearity of the relationship and examined subtype-specific associations and effect modification across strata of breast cancer risk factors, including adiposity.

RESULTS: Over a median 11.5 years of follow-up (IQR, 8.0-16.1 years), high versus low levels of leisure-time physical activity were associated with a 6% (HR, 0.94 [95% CI, 0.89 to 0.99]) and a 10% (HR, 0.90 [95% CI, 0.85 to 0.95]) reduction in breast cancer risk, before and after adjustment for BMI, respectively. Tests of nonlinearity suggested an approximately linear relationship (P_nonlinearity = .94). The inverse association was particularly strong for human epidermal growth factor receptor 2-enriched breast cancer (HR, 0.57 [95% CI, 0.39 to 0.84]; P_het = .07). Associations did not vary significantly across strata of breast cancer risk factors, including subgroups of adiposity.

CONCLUSION: This large, pooled analysis of cohort studies adds to evidence that engagement in higher levels of leisure-time physical activity may lead to reduced premenopausal breast cancer risk.

PMID:38079601 | DOI:10.1200/JCO.23.01101

Int J Surg. 2023 Dec 11. doi: 10.1097/JS9.0000000000000950. Online ahead of print.

ABSTRACT

BACKGROUND: Emerging three-dimensional digital visualization technology (DVT) provides more advantages than traditional microscopy in microsurgery; however, its impact on microsurgeons’ visual and nervous systems and delicate microsurgery is still unclear, which hinders the wider implementation of DVT in digital visualization for microsurgery.

METHODS AND MATERIAL: Forty-two microsurgeons from the *** were enrolled in this prospective self-controlled study. Each microsurgeon consecutively performed 30-minute conjunctival sutures using a three-dimensional digital display and a microscope, respectively. Visual function, autonomic nerve activity, and subjective symptoms were evaluated before and immediately after the operation. Visual functions, including accommodative lag, accommodative amplitude, near point of convergence and contrast sensitivity function (CSF), were measured by an expert optometrist. Heart rate variability (HRV) was recorded by a wearable device for monitoring autonomic nervous activity. Subjective symptoms were evaluated by questionnaires. Microsurgical performance was assessed by the video-based Objective Structured Assessment of Technical Skill (OSATS) tool.

RESULTS: Accommodative lag decreased from 0.63 [0.18] diopters (D) to 0.55 [0.16] D (P=0.014), area under the log CSF increased from 1.49 [0.15] to 1.52 [0.14] (P=0.037), and HRV decreased from 36.00 [13.54] milliseconds (ms) to 32.26 [12.35] ms (P=0.004) after using the DVT, but the changes showed no differences compared to traditional microscopy (P > 0.05). No statistical significance was observed for global OSATS scores between the two rounds of operations (mean difference, 0.05 [95% CI, -1.17 to 1.08] points; P=0.95). Subjective symptoms were quite mild after using both techniques.

CONCLUSIONS: The impact of DVT-based procedures on microsurgeons includes enhanced accommodation and sympathetic activity, but the changes and surgical performance are not significantly different from those of microscopy-based microsurgery. Our findings indicate that short-term use of DVT is reliable for microsurgery and the long-term effect of using DVT deserve more consideration.

PMID:38079600 | DOI:10.1097/JS9.0000000000000950

J Chem Inf Model. 2023 Dec 11. doi: 10.1021/acs.jcim.3c01525. Online ahead of print.

ABSTRACT

Multiclass metabolomic studies have become popular for revealing the differences in multiple stages of complex diseases, various lifestyles, or the effects of specific treatments. In multiclass metabolomics, there are multiple data manipulation steps for analyzing raw data, which consist of data filtering, the imputation of missing values, data normalization, marker identification, sample separation, classification, and so on. In each step, several to dozens of machine learning methods can be chosen for the given data set, with potentially hundreds or thousands of method combinations in the whole data processing chain. Therefore, a clear understanding of these machine learning methods is helpful for selecting an appropriate method combination for obtaining stable and reliable analytical results of specific data. However, there has rarely been an overall introduction or evaluation of these methods based on multiclass metabolomic data. Herein, detailed descriptions of these machine learning methods in multiple data manipulation steps are reviewed. Moreover, an assessment of these methods was performed using a benchmark data set for multiclass metabolomics. First, 12 imputation methods for imputing missing values were evaluated based on the PSS (Procrustes statistical shape analysis) and NRMSE (normalized root-mean-square error) values. Second, 17 normalization methods for processing multiclass metabolomic data were evaluated by applying the PMAD (pooled median absolute deviation) value. Third, different methods of identifying markers of multiclass metabolomics were evaluated based on the CWrel (relative weighted consistency) value. Fourth, nine classification methods for constructing multiclass models were assessed using the AUC (area under the curve) value. Performance evaluations of machine learning methods are highly recommended to select the most appropriate method combination before performing the final analysis of the given data. Overall, detailed descriptions and evaluation of various machine learning methods are expected to improve analyses of multiclass metabolomic data.

PMID:38079572 | DOI:10.1021/acs.jcim.3c01525

Proc Natl Acad Sci U S A. 2023 Dec 19;120(51):e2307804120. doi: 10.1073/pnas.2307804120. Epub 2023 Dec 11.

ABSTRACT

Forms of both simple and complex machine intelligence are increasingly acting within human groups in order to affect collective outcomes. Considering the nature of collective action problems, however, such involvement could paradoxically and unintentionally suppress existing beneficial social norms in humans, such as those involving cooperation. Here, we test theoretical predictions about such an effect using a unique cyber-physical lab experiment where online participants (N = 300 in 150 dyads) drive robotic vehicles remotely in a coordination game. We show that autobraking assistance increases human altruism, such as giving way to others, and that communication helps people to make mutual concessions. On the other hand, autosteering assistance completely inhibits the emergence of reciprocity between people in favor of self-interest maximization. The negative social repercussions persist even after the assistance system is deactivated. Furthermore, adding communication capabilities does not relieve this inhibition of reciprocity because people rarely communicate in the presence of autosteering assistance. Our findings suggest that active safety assistance (a form of simple AI support) can alter the dynamics of social coordination between people, including by affecting the trade-off between individual safety and social reciprocity. The difference between autobraking and autosteering assistance appears to relate to whether the assistive technology supports or replaces human agency in social coordination dilemmas. Humans have developed norms of reciprocity to address collective challenges, but such tacit understandings could break down in situations where machine intelligence is involved in human decision-making without having any normative commitments.

PMID:38079552 | DOI:10.1073/pnas.2307804120

Proc Natl Acad Sci U S A. 2023 Dec 19;120(51):e2309034120. doi: 10.1073/pnas.2309034120. Epub 2023 Dec 11.

ABSTRACT

There is an urgent need for reliable data on the impacts of deforestation on tropical biodiversity. The city-state of Singapore has one of the most detailed biodiversity records in the tropics, dating back to the turn of the 19th century. In 1819, Singapore was almost entirely covered in primary forest, but this has since been largely cleared. We compiled more than 200 y of records for 10 major taxonomic groups in Singapore (>50,000 individual records; >3,000 species), and we estimated extinction rates using recently developed and novel statistical models that account for “dark extinctions,” i.e., extinctions of undiscovered species. The estimated overall extinction rate was 37% (95% CI [31 to 42%]). Extrapolating our Singapore observations to a future business-as-usual deforestation scenario for Southeast Asia suggests that 18% (95% CI [16 to 22%]) of species will be lost regionally by 2100. Our extinction estimates for Singapore and Southeast Asia are a factor of two lower than previous estimates that also attempted to account for dark extinctions. However, we caution that particular groups such as large mammals, forest-dependent birds, orchids, and butterflies are disproportionately vulnerable.

PMID:38079550 | DOI:10.1073/pnas.2309034120

ACS Synth Biol. 2023 Dec 11. doi: 10.1021/acssynbio.3c00504. Online ahead of print.

ABSTRACT

A universal biochemical signal for bacterial cell-cell communication could facilitate programming dynamic responses in diverse bacterial consortia. However, the classical quorum sensing paradigm is that Gram-negative and Gram-positive bacteria generally communicate via homoserine lactones (HSLs) or oligopeptide molecular signals, respectively, to elicit population responses. Here, we create synthetic HSL sensors for Gram-positive Bacillus subtilis 168 using allosteric LuxR-type regulators (RpaR, LuxR, RhlR, and CinR) and synthetic promoters. Promoters were combinatorially designed from different sequence elements (-35, -16, -10, and transcriptional start regions). We quantified the effects of these combinatorial promoters on sensor activity and determined how regulator expression affects its activation, achieving up to 293-fold activation. Using the statistical design of experiments, we identified significant effects of promoter regions and pairwise interactions on sensor activity, which helped to understand the sequence-function relationships for synthetic promoter design. We present the first known set of functional HSL sensors (≥20-fold dynamic range) in B. subtilis for four different HSL chemical signals: p-coumaroyl-HSL, 3-oxohexanoyl-HSL, n-butyryl-HSL, and n-(3-hydroxytetradecanoyl)-HSL. This set of synthetic HSL sensors for a Gram-positive bacterium can pave the way for designable interspecies communication within microbial consortia.

PMID:38079538 | DOI:10.1021/acssynbio.3c00504

PLoS One. 2023 Dec 11;18(12):e0295678. doi: 10.1371/journal.pone.0295678. eCollection 2023.

ABSTRACT

Large datasets with noisy labels and high dimensions have become increasingly prevalent in industry. These datasets often contain errors or inconsistencies in the assigned labels and introduce a vast number of predictive variables. Such issues frequently arise in real-world scenarios due to uncertainties or human errors during data collection and annotation processes. The presence of noisy labels and high dimensions can significantly impair the generalization ability and accuracy of trained models. To address the above issues, we introduce a simple-structured penalized γ-divergence model and a novel meta-gradient correction algorithm and establish the foundations of these two modules based on rigorous theoretical proofs. Finally, comprehensive experiments are conducted to validate their effectiveness in detecting noisy labels and mitigating the curse of dimensionality and suggest that our proposed model and algorithm can achieve promising outcomes. Moreover, we open-source our codes and distinctive datasets on GitHub (refer to https://github.com/DebtVC2022/Robust_Learning_with_MGC).

PMID:38079441 | DOI:10.1371/journal.pone.0295678

PLoS One. 2023 Dec 11;18(12):e0295761. doi: 10.1371/journal.pone.0295761. eCollection 2023.

ABSTRACT

BACKGROUND: Low back pain (LBP) is a highly prevalent condition that substantially impairs individuals’ physical functioning. This highlights the need for effective management strategies to improve patient outcomes. It is, therefore, crucial to have knowledge of physical functioning prognostic factors that can predict outcomes to facilitate the development of targeted treatment plans aiming to achieve better patient outcomes. There is no synthesis of evidence for physical functioning measures as prognostic factors in the LBP population. The objective of this systematic review is to synthesize evidence for physical measures of physical functioning as prognostic factors to predict outcomes in LBP.

METHODS: The protocol is registered in the International Prospective Register of Systematic Reviews and reported in line with the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P). Prospective longitudinal observational studies investigating potential physical prognostic factors in LBP and/or low back-related leg pain population will be included, with no restriction on outcome. Searches will be performed in MEDLINE, EMBASE, Scopus, CINAHL databases, grey literature search using Open Grey System and ProQuest Dissertations and Theses, hand-searching journals, and reference lists of included studies. Two independent reviewers will evaluate the eligibility of studies, extract data, assess risk of bias and quality of evidence. Risk of bias will be assessed using the Quality in Prognostic Studies (QUIPS) tool. Adequacy of clinical, methodological, and statistical homogeneity among included studies will decide quantitative (meta-analysis) or qualitative analysis (narrative synthesis) focused on prognostic factors and strength of association with outcomes. Quality of cumulative evidence will be evaluated using a modified Grading of Recommendations Assessment, Development, and Evaluation (GRADE).

DISCUSSION: Information about prognostic factors can be used to predict outcomes in LBP. Accurate outcome prediction is essential for identifying high-risk patients that allows targeted allocation of healthcare resources, ultimately reducing the healthcare burden.

REGISTRATION: PROSPERO, CRD42023406796.

PMID:38079434 | DOI:10.1371/journal.pone.0295761

JMIR Hum Factors. 2023 Dec 11;10:e52088. doi: 10.2196/52088.

ABSTRACT

BACKGROUND: The digital app SWEPPE (sustainable worker, a digital support for persons with chronic pain and their employers) was developed to improve the support of people with chronic pain in their return-to-work process after sick leave and includes functions such as the action plan, daily self-rating, self-monitoring graphs, the coach, the library, and shared information with the employer.

OBJECTIVE: This study aims to describe the use of the smartphone app SWEPPE among people with chronic pain who have participated in an interdisciplinary pain rehabilitation program.

METHODS: This is a case study including 16 people participating in a feasibility study. The analyses were based on user data collected for 3 months. Quantitative data regarding used functions were analyzed with descriptive statistics, and qualitative data of identified needs of support from the employer were grouped into 8 categories.

RESULTS: Self-monitoring was used by all participants (median 26, IQR 8-87 daily registrations). A total of 11 (N=16, 69%) participants set a work-related goal and performed weekly evaluations of goal fulfillment and ratings of their work ability. In total, 9 (56%) participants shared information with their employer and 2 contacted the coach. A total of 15 (94%) participants identified a total of 51 support interventions from their employer. Support to adapt to work assignments and support to adapt to work posture were the 2 biggest categories. The most common type of support identified by 53% (8/15) of the participants was the opportunity to take breaks and short rests.

CONCLUSIONS: Participants used multiple SWEPPE functions, such as daily self-registration, goal setting, self-monitoring, and employer support identification. This shows the flexible nature of SWEPPE, enabling individuals to select functions that align with their needs. Additional research is required to investigate the extended use of SWEPPE and how employers use shared employee information.

PMID:38079212 | DOI:10.2196/52088

JMIR Form Res. 2023 Dec 11;7:e49179. doi: 10.2196/49179.

ABSTRACT

BACKGROUND: The prevention of oral health diseases is a key public health issue and a major challenge for racial and ethnic minority groups, who often face barriers in accessing dental care. Daily toothbrushing is an important self-care behavior necessary for sustaining good oral health, yet engagement in regular brushing remains a challenge. Identifying strategies to promote engagement in regular oral self-care behaviors among populations at risk of poor oral health is critical.

OBJECTIVE: The formative research described here focused on creating messages for a digital oral self-care intervention targeting a racially and ethnically diverse population. Theoretically grounded strategies (reciprocity, reciprocity-by-proxy, and curiosity) were used to promote engagement in 3 aspects: oral self-care behaviors, an oral care smartphone app, and digital messages. A web-based participatory co-design approach was used to develop messages that are resource efficient, appealing, and novel; this approach involved dental experts, individuals from the general population, and individuals from the target population-dental patients from predominantly low-income racial and ethnic minority groups. Given that many individuals from racially and ethnically diverse populations face anonymity and confidentiality concerns when participating in research, we used an approach to message development that aimed to mitigate these concerns.

METHODS: Messages were initially developed with feedback from dental experts and Amazon Mechanical Turk workers. Dental patients were then recruited for 2 facilitator-mediated group webinar sessions held over Zoom (Zoom Video Communications; session 1: n=13; session 2: n=7), in which they provided both quantitative ratings and qualitative feedback on the messages. Participants interacted with the facilitator through Zoom polls and a chat window that was anonymous to other participants. Participants did not directly interact with each other, and the facilitator mediated sessions by verbally asking for message feedback and sharing key suggestions with the group for additional feedback. This approach plausibly enhanced participant anonymity and confidentiality during the sessions.

RESULTS: Participants rated messages highly in terms of liking (overall rating: mean 2.63, SD 0.58; reciprocity: mean 2.65, SD 0.52; reciprocity-by-proxy: mean 2.58, SD 0.53; curiosity involving interactive oral health questions and answers: mean 2.45, SD 0.69; curiosity involving tailored brushing feedback: mean 2.77, SD 0.48) on a scale ranging from 1 (do not like it) to 3 (like it). Qualitative feedback indicated that the participants preferred messages that were straightforward, enthusiastic, conversational, relatable, and authentic.

CONCLUSIONS: This formative research has the potential to guide the design of messages for future digital health behavioral interventions targeting individuals from diverse racial and ethnic populations. Insights emphasize the importance of identifying key stimuli and tasks that require engagement, gathering multiple perspectives during message development, and using new approaches for collecting both quantitative and qualitative data while mitigating anonymity and confidentiality concerns.

PMID:38079204 | DOI:10.2196/49179