Citations

Abstract Objectives Breast milk is the recommended nutritional source for newborns and has been associated with decreased morbidity in low‐birth‐weight and preterm infants. In situations where breast milk is not available, donor breast milk is an alternative. Milk banking is becoming increasingly common worldwide to meet this need. Although the benefits of donor breast milk for the recipient infant are well established, the health impact on the breast milk donor and the infant of the breast milk donor is an area of current research. We aim to synthesize and evaluate the available evidence regarding the impact of donating breast milk on the health, lactation, and well‐being of the breast milk donor, and the health and growth of the infant of the breast milk donor. Methods We will search electronic databases, grey literature, and the websites of relevant international organizations. We will include studies that involve lactating women and their infants, healthy or with health conditions, who donate breast milk, without restrictions on study date, language, or study design. If sufficient homogeneity exists between studies, we will complete meta‐analyses. We will evaluate the risk of bias using the Risk of Bias tool or the Cochrane Risk of Bias in Non‐Randomized Studies tool. We will evaluate the overall certainty of the evidence using the Grading of Recommendations Assessment, Development, and Evaluation approach. Conclusion In this systematic review and meta‐analysis, we will summarize the current literature regarding the effects of human milk donation on human milk donors and their infants.

Abstract Objectives Human milk has been shown to reduce severe morbidity in preterm/low‐birth‐weight infants and is therefore the recommended nutritional source. When infants cannot receive maternal milk, donor human milk (DHM) is recommended. The use of human milk banking facilities is increasing to meet the need for DHM. DHM is unique compared to maternal milk as it must be processed and stored. The processing and storage of animal milk has been more rigorously studied than human milk and can serve as proxy to create DHM banking guidelines. Methods and Analysis We will search electronic databases, grey literature, and the websites of relevant international organizations. We will include studies that evaluated the impact of storage, handling, and treatment on the nutritional quality and safety of animal milk. We will not restrict study date, language, or design. If sufficient homogeneity exists between studies, we will conduct a meta‐analysis. We will evaluate the methodological quality of each study using the SYRCLE's (Systematic Review Centre for Laboratory Animal Experimentation) risk of bias tool. (1) We will evaluate the overall certainty of the evidence using the Grading of Recommendations Assessment, Development, and Evaluation approach. Conclusion In this systematic review and meta‐analysis, commissioned by the World Health Organization, we will synthesize the available literature regarding the impact of various storage, handling, and treatment practices on the nutritional quality and safety of animal milk.

Abstract Background and Aims The advent of 3D printing has revolutionized plastic surgery and prosthetic devices, providing personalized solutions for patients with traumatic injuries, deformities, and appearance‐related conditions. This review offers a comprehensive overview of 3D printing's applications, advantages, limitations, and future prospects in these fields. Methods A literature search was conducted in PubMed, Google Scholar, and Scopus for studies on 3D printing in plastic surgery. Results 3D printing has significantly contributed to personalized medical interventions, with benefits like enhanced design flexibility, reduced production time, and improved patient outcomes. Using computer‐aided design (CAD) software, precise models tailored to a patient's anatomy can be created, ensuring better fit, functionality, and comfort. 3D printing allows for intricate geometries, leading to improved aesthetic outcomes and patient‐specific prosthetic limbs and orthoses. The historical development of 3D printing, key milestones, and breakthroughs are highlighted. Recent progress in bioprinting and tissue engineering shows promising applications in regenerative medicine and transplantation. The integration of AI and automation with 3D printing enhances surgical planning and outcomes. Emerging trends in patient‐specific treatment planning and precision medicine are potential game‐changers. However, challenges like technical considerations, economic implications, and ethical issues exist. Addressing these challenges and advancing research in materials, design processes, and long‐term outcomes are crucial for widespread adoption. Conclusion The review underscores the increasing adoption of 3D printing in healthcare and its impact on plastic surgery and prosthetic devices. It emphasizes the importance of evaluating the current state and addressing knowledge gaps through future research to foster further advancements

AbstractBackgroundIntensity Modulated Proton Therapy (IMPT) is a sophisticated radiation treatment allowing for precise dose distributions. However, conventional spot selection strategies in IMPT face challenges, particularly with minimum monitor unit (MU) constraints, affecting planning quality and efficiency.PurposeThis study introduces an innovative Two‐Stage Mixed Integer Linear Programming (MILP) method to optimize spot intensity in IMPT with Lower Bound (LB) constraints. This method seeks to improve treatment planning efficiency and precision, overcoming limitations of existing strategies.MethodsOur approach evaluates prevalent IMPT spot selection strategies, identifying their limitations, especially concerning MU constraints. We integrated LB constraints into a MILP framework, using a novel three‐phase strategy for spot pool selection, to enhance performance over traditional heuristic methods and L1 + L∞ strategies. The method's efficacy was tested in eight study cases, using Dose‐Volume Histograms (DVHs), spot selection efficiency, and computation time analysis for benchmarking against established methods.ResultsThe proposed method showed superior performance in DVH quality, adhering to LB constraints while maintaining high‐quality treatment plans. It outperformed existing techniques in spot selection, reducing unnecessary spots and balancing precision with efficiency. Cases studies confirmed the method's effectiveness in producing clinically feasible plans with enhanced dose distributions and reduced hotspots, especially in cases with elevated LB constraints.ConclusionsOur Two‐Stage MILP strategy signifies a significant advancement in IMPT treatment planning. By incorporating LB constraints directly into the optimization process, it achieves superior plan quality and deliverability compared to current methods. This approach is particularly advantageous in clinical settings requiring minimum spot number and high MU LB constraints, offering the potential for improved patient outcomes through more precise and efficient radiation therapy plans.

AbstractBackgroundInserting noise into existing patient projection data to simulate lower‐radiation‐dose exams has been frequently used in traditional energy‐integrating‐detector (EID)‐CT to optimize radiation dose in clinical protocols and to generate paired images for training deep‐learning‐based reconstruction and noise reduction methods. Recent introduction of photon counting detector CT (PCD‐CT) also requires such a method to accomplish these tasks. However, clinical PCD‐CT scanners often restrict the users access to the raw count data, exporting only the preprocessed, log‐normalized sinogram. Therefore, it remains a challenge to employ projection domain noise insertion algorithms on PCD‐CT.PurposeTo develop and validate a projection domain noise insertion algorithm for PCD‐CT that does not require access to the raw count data.Materials and MethodsA projection‐domain noise model developed originally for EID‐CT was adapted for PCD‐CT. This model requires, as input, a map of the incident number of photons at each detector pixel when no object is in the beam. To obtain the map of incident number of photons, air scans were acquired on a PCD‐CT scanner, then the noise equivalent photon number (NEPN) was calculated from the variance in the log normalized projection data of each scan. Additional air scans were acquired at various mA settings to investigate the impact of pulse pileup on the linearity of NEPN measurement. To validate the noise insertion algorithm, Noise Power Spectra (NPS) were generated from a 30 cm water tank scan and used to compare the noise texture and noise level of measured and simulated half dose and quarter dose images. An anthropomorphic thorax phantom was scanned with automatic exposure control, and noise levels at different slice locations were compared between simulated and measured half dose and quarter dose images. Spectral correlation between energy thresholds T1 and T2, and energy bins, B1 and B2, was compared between simulated and measured data across a wide range of tube current. Additionally, noise insertion was performed on a clinical patient case for qualitative assessment.ResultsThe NPS generated from simulated low dose water tank images showed similar shape and amplitude to that generated from the measured low dose images, differing by a maximum of 5.0% for half dose (HD) T1 images, 6.3% for HD T2 images, 4.1% for quarter dose (QD) T1 images, and 6.1% for QD T2 images. Noise versus slice measurements of the lung phantom showed comparable results between measured and simulated low dose images, with root mean square percent errors of 5.9%, 5.4%, 5.0%, and 4.6% for QD T1, HD T1, QD T2, and HD T2, respectively. NEPN measurements in air were linear up until 112 mA, after which pulse pileup effects significantly distort the air scan NEPN profile. Spectral correlation between T1 and T2 in simulation agreed well with that in the measured data in typical dose ranges.ConclusionsA projection‐domain noise insertion algorithm was developed and validated for PCD‐CT to synthesize low‐dose images from existing scans. It can be used for optimizing scanning protocols and generating paired images for training deep‐learning‐based methods.

Abstract Background Polygenic risk scores (PRS) aggregate the contribution of many risk variants to provide a personalized genetic susceptibility profile. Since sample sizes of glioma genome-wide association studies (GWAS) remain modest, there is a need to efficiently capture genetic risk using available data. Methods We applied a method based on continuous shrinkage priors (PRS-CS) to model the joint effects of over 1 million common variants on disease risk and compared this to an approach (PRS-CT) that only selects a limited set of independent variants that reach genome-wide significance (P < 5 × 10–8). PRS models were trained using GWAS stratified by histological (10 346 cases and 14 687 controls) and molecular subtype (2632 cases and 2445 controls), and validated in 2 independent cohorts. Results PRS-CS was generally more predictive than PRS-CT with a median increase in explained variance (R2) of 24% (interquartile range = 11–30%) across glioma subtypes. Improvements were pronounced for glioblastoma (GBM), with PRS-CS yielding larger odds ratios (OR) per standard deviation (SD) (OR = 1.93, P = 2.0 × 10–54 vs. OR = 1.83, P = 9.4 × 10–50) and higher explained variance (R2 = 2.82% vs. R2 = 2.56%). Individuals in the 80th percentile of the PRS-CS distribution had a significantly higher risk of GBM (0.107%) at age 60 compared to those with average PRS (0.046%, P = 2.4 × 10–12). Lifetime absolute risk reached 1.18% for glioma and 0.76% for IDH wildtype tumors for individuals in the 95th PRS percentile. PRS-CS augmented the classification of IDH mutation status in cases when added to demographic factors (AUC = 0.839 vs. AUC = 0.895, PΔAUC = 6.8 × 10–9). Conclusions Genome-wide PRS has the potential to enhance the detection of high-risk individuals and help distinguish between prognostic glioma subtypes.

Abstract Background Tumor microenvironment (TME) characteristics including tumor stroma ratio (TSR), tumor budding (TB), and tumor-infiltrating lymphocytes (TILs) were examined in resected gastric cancer. These TME features have been shown to indicate metastatic potential in colon cancer, and intestinal-type gastric cancer (IGC) has pathological similarities with that malignancy. Methods TSR, TB, and TILs were quantified in routine histological sections from 493 patients with IGC who underwent radical resection at 2 university hospitals in China from 2010 to 2016. TME variables were dichotomized as follows: TSR (50%), TILs (median), TB per international guidelines (4 buds/0.785mm2), and platelet-lymphocyte ratio (PLR) per survival ROC. Association of TME features with patient clinicopathological characteristics, time-to-recurrence (TTR), and cancer-specific-survival (CSS) were examined using univariate and multivariate analysis, including a relative contribution analysis by Cox regression. Results Patients whose tumors showed high TSR or high TB or low TILs were each significantly associated with increased T and N stage, higher histological grade, and poorer TTR and CSS at 5 years. Only TSR and N stage were independently associated with TTR and CSS after adjustment for covariates. PLR was only independently associated with TTR after adjustment for covariates. Among the variables examined, only TSR was significantly associated with both TTR (HR 1.72, 95% CI, 1.14-2.60, P = .01) and CSS (HR 1.62, 95% CI, 1.05-2.51, P = .03) multivariately. Relative contribution to TTR revealed that the top 3 contributors were N stage (45.1%), TSR (22.5%), and PLR (12.9%), while the top 3 contributors to CSS were N stage (59.9%), TSR (14.7%), and PLR (10.9%). Conclusions Among the examined TME features, TSR was the most robust for prognostication and was significantly associated with both TTR and CSS. Furthermore, the relative contribution of TSR to patient TTR and CSS was second only to nodal status.

Abstract Objective To characterize the long‐term (56‐week) benefits of continuous onabotulinumtoxinA treatment response in individuals with chronic migraine (CM) who achieved reduction to <15 headache days/month with treatment. Background There are limited data exploring reductions in monthly headache days to levels consistent with episodic migraine among those experiencing CM. Understanding the impact of sustained preventive treatment response in CM can provide important information about the impact of successful therapy. Methods The two Phase 3 REsearch Evaluating Migraine Prophylaxis Therapy trials of onabotulinumtoxinA in adults included a 24‐week, randomized, double‐blind, placebo‐controlled phase and a 32‐week open‐label phase. Data were pooled to determine proportions of individuals with <15 headache days/month while on treatment during several time periods in the double‐blind phase (Weeks 21–24; any 12 consecutive weeks; Weeks 13–24) and the entire study (Weeks 53–56; any 12 consecutive weeks; any 4‐week period). We assessed the long‐term impact on mean monthly headache days and changes from baseline on the six‐item Headache Impact Test (HIT‐6) and Migraine‐Specific Quality of Life questionnaire version 2.1 (MSQv2.1). Results We analyzed 1384 participants with chronic migraine (double‐blind: onabotulinumtoxinA, n = 688; placebo, n = 696; open‐label: n = 688 [onabotulinumtoxinA]). The discontinuation rates prior to the completion of the full 56‐week treatment period for onabotulinumtoxinA and placebo were 25.4% ( n = 175) and 29.3% ( n = 204), respectively. During Weeks 13–24 of the double‐blind phase, significantly more onabotulinumtoxinA‐treated (386/688 [56.1%]) than placebo‐treated (342/696 [49.1%]) individuals had <15 headache days/month ( p = 0.010), with fewer monthly headache days for onabotulinumtoxinA versus placebo responders. The proportions of participants achieving <15 monthly headache days with onabotulinumtoxinA were 60.9% (419/688) at Weeks 25–56, 81.1% (558/688) at Weeks 53–56, and 79.4% (546/688) during any consecutive 12‐week period. Mean changes from baseline on the HIT‐6 and MSQv2.1 questionnaire surpassed within‐group minimal important difference thresholds in all periods. At Week 24, onabotulinumtoxinA‐treated participants who achieved <15 monthly headache days during Weeks 21–24 had a greater mean HIT‐6 score reduction (−6.5 vs. −1.4) and greater mean MSQv2.1 Role‐Function Restrictive score improvements (21.3 vs. 6.4) than those who did not achieve <15 monthly headache days during the same period. Conclusions Participants who achieved <15 monthly headache days with onabotulinumtoxinA treatment achieved meaningful benefits in headache‐related disability and migraine‐specific quality of life compared with those who remained at or above the 15–monthly headache days threshold. Sustained benefits observed over 56 weeks support long‐term onabotulinumtoxinA use for the prevention of CM.

Abstract Background A previous phase IB/II study of nab‐paclitaxel + cisplatin + gemcitabine (NABPLAGEM) in 25 patients with previously untreated metastatic pancreatic ductal adenocarcinoma (PDAC) demonstrated favorable results. This phase II study was conducted to further evaluate the safety, efficacy, and impact on quality of life (QOL) of NABPLAGEM in a multi‐center setting. Methods Participants were ≥18 years; had measurable PDAC; Karnofsky performance status of ≥70%; life expectancy ≥12 weeks; <grade 2 pre‐existing peripheral neuropathy, and adequate hematologic, hepatic, and renal function. Study treatment included nab‐paclitaxel (125 mg/m 2 ), cisplatin (25 mg/m 2 ), and gemcitabine (1000 mg/m 2 ), on Days 1 and 8 of a 21‐day cycle. The primary endpoint was 12‐month overall survival (OS). Results A total of 42 patients were enrolled with a median age of 66.8 years, a majority were male (66.7%) and white (76.2%). Treatment‐related adverse events ≥grade 3 were: thrombocytopenia (64.3%), anemia (47.6%), and neutropenia (26.1%). OS at 12 months was 38.1%, with 12% of patients alive as of last contact (20–40+ months). In 37 evaluable participants responses included: partial response (40.5%), stable disease (43.2%), and progressive disease (16.2%), with overall response rate of 40.5% and DCR of 88.6%. Statistically significant improvement in QOL was reported during the first three cycles. Conclusion The primary objective of achieving a 12‐month survival comparable to the previous phase Ib/II NABPLAGEM PCRT 12‐001 was not achieved in this trial. NABPLAGEM has efficacy in advanced PDAC, demonstrating early disease control noted by improvement in patient symptoms, decrease in tumor volume and tumor markers within the first 9 weeks. The activity and tolerability make this a suitable regimen for further testing in the neoadjuvant setting.