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Abstract Background Pathological complete response (pCR) is associated with favorable prognosis in patients with triple-negative breast cancer (TNBC). However, only 30–40% of TNBC patients treated with neoadjuvant chemotherapy (NAC) show pCR, while the remaining 60–70% show residual disease (RD). The role of the tumor microenvironment in NAC response in patients with TNBC remains unclear. In this study, we developed a machine learning-based two-step pipeline to distinguish between various histological components in hematoxylin and eosin (H&E)-stained whole slide images (WSIs) of TNBC tissue biopsies and to identify histological features that can predict NAC response. Methods H&E-stained WSIs of treatment-naïve biopsies from 85 patients (51 with pCR and 34 with RD) of the model development cohort and 79 patients (41 with pCR and 38 with RD) of the validation cohort were separated through a stratified eightfold cross-validation strategy for the first step and leave-one-out cross-validation strategy for the second step. A tile-level histology label prediction pipeline and four machine-learning classifiers were used to analyze 468,043 tiles of WSIs. The best-trained classifier used 55 texture features from each tile to produce a probability profile during testing. The predicted histology classes were used to generate a histology classification map of the spatial distributions of different tissue regions. A patient-level NAC response prediction pipeline was trained with features derived from paired histology classification maps. The top graph-based features capturing the relevant spatial information across the different histological classes were provided to the radial basis function kernel support vector machine (rbfSVM) classifier for NAC treatment response prediction. Results The tile-level prediction pipeline achieved 86.72% accuracy for histology class classification, while the patient-level pipeline achieved 83.53% NAC response (pCR vs. RD) prediction accuracy of the model development cohort. The model was validated with an independent cohort with tile histology validation accuracy of 83.59% and NAC prediction accuracy of 81.01%. The histological class pairs with the strongest NAC response predictive ability were tumor and tumor tumor-infiltrating lymphocytes for pCR and microvessel density and polyploid giant cancer cells for RD. Conclusion Our machine learning pipeline can robustly identify clinically relevant histological classes that predict NAC response in TNBC patients and may help guide patient selection for NAC treatment.

Abstract Background Maternity leave policies are designed to protect gender equality and the health of mothers in the workforce and their children. However, maternity leave schemes are often linked to jobs in the formal sector economy. In low- and middle-income countries a large share of women work in the informal sector, and are not eligible to such benefit. This is worrisome from a social justice and a policy perspective and suggests the need for intervening. Costing the implementation of potential interventions is needed for facilitating informed decisions by policy makers. Methods We developed and applied a costing methodology to assess the cost of a maternity leave cash transfer to be operated in the informal sector of the economy in Brazil and Ghana, two countries with very different employment structures and socioeconomic contexts. We conducted sensitivity analysis by modeling different numbers of weeks covered. Results In Brazil, the cost of the maternity cash transfer would be between 0.004% and 0.02% of the GDP, while in Ghana it would range between 0.076% and 0.28% of the GDP. The relative cost of rolling out a maternity intervention in Brazil is between 2.2 to 3.2 times the cost in Ghana depending on the benchmark used to assess the welfare measure. The differences in costs between countries was related to differences in labor market structure as well as demographic characteristics. Conclusions Findings show how a standard methodology that relies on routinely available information is feasible and could assist policymakers in estimating the costs of supporting a maternity cash transfer for women employed in the informal sector, such intervention is expected to contribute to social justice, gender equity, and health trajectories.

Abstract Background Uncontrolled growth in solid breast cancer generates mechanical compression that may drive the cancer cells into a more invasive phenotype, but little is known about how such compression affects the key events and corresponding regulatory mechanisms associated with invasion of breast cancer cells including cellular behaviors and matrix degradation. Results Here we show that compression enhanced invasion and matrix degradation of breast cancer cells. We also identified Piezo1 as the putative mechanosensitive cellular component that transmitted compression to not only enhance the invasive phenotype, but also induce calcium influx and downstream Src signaling. Furthermore, we demonstrated that Piezo1 was mainly localized in caveolae, and both Piezo1 expression and compression-enhanced invasive phenotype of the breast cancer cells were reduced when caveolar integrity was compromised by either knocking down caveolin1 expression or depleting cholesterol content. Conclusions Taken together, our data indicate that mechanical compression activates Piezo1 channels to mediate enhanced breast cancer cell invasion, which involves both cellular events and matrix degradation. This may be a critical mechanotransduction pathway during breast cancer metastasis, and thus potentially a novel therapeutic target for the disease.

Abstract Background The economic cost of not breastfeeding in Indonesia is estimated at US$1.5–9.4 billion annually, the highest in South East Asia. Half of the 33.6 million working women of reproductive age (WRA) in Indonesia (15–49 years) are informal employees, meaning they are working as casual workers or they are self-employed (small scale business) and assisted by unpaid/family worker(s). No specific maternity protection entitlements are currently available for WRA working informally in Indonesia. This study aims to estimate the financing need of providing maternity leave cash transfer (MCT) for WRA working in the informal sector in Indonesia. Method The costing methodology used is the adapted version of the World Bank methodology by Vilar-Compte et al, following pre-set steps to estimate costs using national secondary data. We used the 2018 Indonesian National Socio-Economic Survey to estimate the number of women working informally who gave birth within the last year. The population covered, potential cash transfer’s unitary cost, the incremental coverage of the policy in terms of time and coverage, and the administrative costs were used to estimate the cost of MCT for the informal sector. Result At 100% coverage for 13 weeks of leave, the yearly financing need of MCT ranged from US$175million (US$152/woman) to US$669million (US$583/woman). The share of the yearly financing need did not exceed 0.5% of Indonesian Gross Domestic Product (GDP). Conclusions The yearly financing need of providing MCT for eligible WRA working in the informal sector is economically attractive as it amounts to less than 0.5% of GDP nominal of Indonesia. While such a program would be perceived as a marked increase from current public health spending at the onset, such an investment could substantially contribute to the success of breastfeeding and substantial corresponding public health savings given that more than half of working Indonesian WRA are employed in the informal sector. Such policies should be further explored while taking into consideration realistic budget constraints and implementation capacity.

Abstract Background The patient-reported outcome measures (PROMs), patient-reported experience measure (PREMs) and Effectiveness Programme (PPEP) launched with the aim of supporting all National Health Service Wales (NHS Wales) organisations to collect PROMs and PREMs across a range of conditions. The aim is to collect generic and condition-specific PROMs and PREMs electronically from every secondary care patient in Wales to provide a measure that can be used to determine the clinical and cost-effectiveness of treatments and services. This study reports on the experience of the PPEP in developing an electronic platform suitable for large-scale data collection, storage, analysis and reporting and identifies the problems encountered and solutions implemented using a generic PROM survey as an example. Methods The generic PROM survey is available in English and Welsh and consists of a consent section and three components: the EQ-5D-5L tool, the Work Productivity and Activity Impairment (WPAI) tool and a number of “about you” questions. The “about you” questions are designed to assess factors which may affect patient health and outcomes such as information on height, weight, smoking history, exercise levels and alcohol consumption. A dedicated PROM database was built, and links between the e-PROM platform and other key clinical databases within NHS Wales were developed. Results Pilot testing of the unvalidated sections of the generic electronic PROM found that most of the questions were well understood and easy to answer: however, feedback suggested some improvements and changes were required, specifically around questions relating to alcohol and exercise. Electronic PROM collection has been initiated in six of the seven health boards in Wales and at-home collection initiated in three health boards. More than 9300 patients have completed a PROM survey. Early results from one Health Board show that patients took approximately 10 min to complete the questionnaire with most patients answering an average of 94.7% of the questions. Conclusions Successful implementation of a PROM collection programme is dependent on a number of factors including close collaboration with clinicians, analysts, IT specialists and patients to ensure that any electronic system of PROM collection is fit for purpose and user friendly both for patients and clinicians.