BackgroundThe coronavirus disease 2019 (COVID-19), declared a pandemic in March 2020, has affected the entire healthcare system, including the surgical practice. Guidelines for the management of surgical patients during this COVID-19 era need to be established to provide timely yet safe surgical care. In this study, we aimed to evaluate the outcomes of the COVID-19 testing algorithm established for surgery patients presenting to a tertiary care hospital in Karachi, Pakistan, and to compare the outcomes among patients who underwent elective versus emergency surgery.MethodologyThis is a cross-sectional study conducted at a tertiary care hospital in Pakistan to apply and assess the outcomes of the COVID-19 testing algorithm established for patients presenting for surgery. We included all patients who underwent any surgery from May to October 2020. The total sample size was 6,846. The data were analyzed using SPSS version 23 . The categorical variables were assessed using the chi-square or Fisher’s exact test. A p-value of <0.05 was considered significant.ResultsA total of 6,846 surgeries were performed from May 1 to October 31, 2020. In total, 74% of the surgeries were elective procedures. We observed that a significantly higher proportion of emergency surgery patients tested positive for COVID-19 (4.2%) compared to elective surgery patients . A higher proportion of surgeries were performed in September  and October  while the lowest number of surgeries were performed in May . From week one to week five, a higher proportion of emergency surgeries were performed (32%) compared to elective surgeries (25%). Only 1.9% of the patients who were undergoing surgery were COVID-19 positive, with the highest number of COVID-19 cases presenting in June. Overall, 74 (4.2%) of the COVID-19-positive patients underwent emergency surgeries.ConclusionsThe timely establishment of well-defined guidelines for surgical management during the pandemic allowed us to provide timely and effective surgical care to patients with the priority of minimizing the spread of COVID-19 and preventing unnecessary deferral of surgeries. The disease declared as coronavirus disease 2019 (COVID-19) by the World Health Organization (WHO) was initially known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) . On MarcWith the increasing incidence of COVID-19 in different parts of the world, the COVID-19 pandemic has disrupted healthcare services, especially their capability to manage affected people and the ability to provide standard treatment for critically ill patients in a safe environment . SimilarIn a lower-middle-income country (LMIC) such as Pakistan with limited healthcare facilities, the effect of COVID-19 has been severe. Effective management and implementation of processes and planning are imperative . To perfObjectivesIn this study, we aimed to evaluate the outcomes  of a COVID-19 testing algorithm established for surgery patients presenting to a tertiary care hospital in Karachi, Pakistan. In addition, we compared the outcomes of patients who underwent elective versus emergency surgery.This cross-sectional study was conducted in the Department of Surgery of a tertiary care hospital in Karachi, Pakistan. Non-probability consecutive sampling technique was employed to recruit patients from May to October 2020. All patients presenting for surgical procedures from May to October 2020 were included. Patients who left against medical advice or with missing information were excluded.Description of the COVID-19 testing algorithm for surgery patientsAccording to the COVID-19 testing algorithm (Appendix 1) for surgery patients, patients were screened for COVID-19 using the Patient Under Investigation (PUI) tracking form by a nurse. This form recorded all COVID-19-related information of the patient. The form included the patients’ demographics and history of any of the following symptoms within 14 days: fever, cough, shortness of breath, myalgia, and sore throat. Moreover, it also included any history of travel from another country, direct contact with a person with confirmed/probable COVID-19, healthcare worker returning from a congregation, and engagement in public dealing.Black code refers to an elective surgery or elective procedure that is scheduled in advance because it does not involve a medical emergency. Orange code refers to an urgent surgery that can wait until the patient is medically stable but should generally be done within two days. Red code refers to an emergency surgery that must be performed without delay; in this code, the patient has no choice other than immediate surgery if permanent disability or death is to be avoided.According to this algorithm, for black code elective cases, in a non-suspected COVID-19 patient, the testing is done at the COVID-19 test center. Patients with low to medium risk or those who are asymptomatic are sent for pooled COVID-19 polymerase chain reaction (PCR) test. However, patients with high risk are sent for individual COVID-19 PCR testing. Patients with a negative COVID-19 result proceed with the surgery. For patients with a positive COVID-19 result, surgery is either deferred, or if the surgery cannot be deferred, they undergo surgery in a negative pressure flow operating room (OR) with full PPE (N95 masks and face and eye shields). Following the surgery, they are admitted to the COVID-19 Diagnostic and Treatment Zone (CDTZ). However, if the patient is a COVID-19-suspected case at baseline, surgery is either deferred, or if it cannot be deferred, they are admitted to the CDTZ.For orange code emergency cases, the same procedure of COVID-19 screening is followed and the tracking PUI form is documented by the nurse. COVID-19-suspected cases are admitted in the CDTZ (under the care of the primary team) and non-suspected cases are admitted in private rooms. COVID-19 testing is done in a designated inpatient area by trained surgery residents or nurses with appropriate PPE, including N95 masks and face and eye shields. Those who test negative are admitted and proceed with surgery as indicated. For those who test positive, the surgery is either deferred or patients undergo surgery in a negative pressure flow OR with full PPE (N95 masks and face and eye shields) and are admitted to the CDTZ after surgery.For red code emergency cases, the COVID-19 test is performed in the emergency room (ER) or the OR. These patients undergo surgery in a negative pressure flow OR with full PPE (N95 masks and face and eye shields) and are admitted to the CDTZ if the COVID-19 test result is not available by the time surgery concludes. Those who test negative are shifted to the service line bed while those who test positive remain in the CDTZ after surgery.The data for the study were collected on a Proforma from the patients’ medical records during May and October 2020. Data regarding patients’ demographic, type of case , type of surgery, COVID-19 screening (non-suspected or suspected case), COVID-19 results (positive or negative), surgery outcome, and classification of surgery according to specialties were collected.clinicaltrial.gov NCT04911868. All patient information was kept securely in lock and key. The database was password protected and only accessible to the research team. All the patient information was de-identified. When patients were admitted to the hospital consent was taken from them. They were informed that their information will be used for research purposes and the study results will be published in group form and no individual information will be disclosed.Because there was no interaction with the patients and all the information was collected from hospital medical records, an ethical exemption was sought from the Aga Khan University Ethical Review Committee (ERC #2020-5014-11563). This study is registered in The data were analyzed using SPSS version 22 . Descriptive for qualitative variables was reported as frequency and percentages and was assessed using the chi-square and Fisher’s exact test. Bar charts for categorical variables and histogram/line graphs were presented for quantitative variables. A p-value of <0.05 was kept significant throughout the study. This work has been reported in line with the Strengthening The Reporting Of Cohort Studies in Surgery (STROCSS) criteria .A total of 6,846 surgeries were performed from May to October 2020. Table Figure Table Figure Table The COVID-19 pandemic has placed unprecedented demands on the healthcare system. Although surgical services have been affected indirectly, they have not been spared. Organizations such as the Centers for Disease Control and Prevention (CDC), WHO, American College of Surgeons have recommended postponing/rescheduling elective surgeries to prevent the spread of the virus . The canThe first COVID-19 case in Pakistan was diagnosed in March 2020, and the number of cases peaked in June and July 2020 with a downward trend thereafter . This waThis study found that the number of elective surgeries performed was the lowest in May 2020, with the majority of surgeries performed during this time being emergency procedures. Elective surgeries were postponed/deferred due to an increase in COVID-19 cases in May according to our surgical management guidelines. Our management recommendations were consistent with those stated in the literature, in that elective operations should be deferred, and only urgent, life-saving procedures and oncologic surgeries should be performed that are associated with worse outcomes if delayed .All the cases presenting for elective surgery were labeled as black code while the majority of cases presenting for emergency surgery were labeled as orange code followed by red code. Following the aforementioned algorithm, only 1.4% of the patients who underwent surgery were positive for COVID-19. The majority of these patients underwent emergency surgery, and a greater proportion belonged to the red code. Surgeries on COVID-19 patients were performed in negative pressure flow OR with full PPE (including N95 masks and face and eye shields). Such patients were then admitted to the CTDZ. Our surgical management algorithm followed the guidelines similar to those from high-volume facilities in high-income countries (HICs) that include patient triaging based on symptoms and disease; patient segregation based on COVID-19 status; PPE use based on the WHO recommendations; limiting aerosolizing procedures in the OR; use of negative-pressure ORs for COVID-19-positive patients; use of negative-pressure intensive care units for postoperative patients (in case these patients develop COVID-19 infection); and immediate postoperative step-down areas . The praTo our knowledge, this is the first study from Pakistan describing a COVID-19 testing algorithm in surgery patients. There are some limitations of the study. Postsurgical testing was not done and the spread of COVID-19 during hospitalization for surgery was not assessed. Moreover, the survival rates and length of hospital stay of the patients who had COVID-19 while undergoing surgery were not available and therefore not reported in this study.Our management recommendations are in line with those reported in the literature, suggesting that urgent, life-saving procedures and oncologic surgeries with worse outcomes should be considered timely. During the pandemic, we were able to provide timely and effective surgical care to patients by establishing well-defined surgical management guidelines that were consistent with the guidelines recommended by HICs. This allowed us to prioritize preventing the spread of COVID while also avoiding unnecessary surgery deferral. Other LMICs can use a similar algorithm to establish a surgical management strategy during the pandemic.


Declines in physical and cognitive functions are recognized as important risk factors for falls in older adults. Promising evidence suggests that interactive game-based systems that allow simultaneous physical and cognitive exercise are a potential approach to enhance exercise adherence and reduce fall risk in older adults. However, a limited number of studies have reported the development of a combined physical-cognitive game-based training system for fall risk reduction in older adults.The aim of this study is to develop and evaluate the usability of an interactive physical-cognitive game-based training system (game-based exercise) for older adults.In the development phase (Part I), a game-based exercise prototype was created by integrating knowledge and a literature review as well as brainstorming with experts on effective fall prevention exercise for older adults. The output was a game-based exercise prototype that covers crucial physical and cognitive components related to falls. In the usability testing (Part II), 5 games  with three difficulty levels  were tested in 5 older adults . After completing the games, participants rated their enjoyment level while engaging with the games using the Physical Activity Enjoyment Scale (PACES) and commented on the games. Descriptive statistics were used to describe the participants’ characteristics and PACES scores.The results showed that the average PACES score was 123 out of 126 points overall and between 6.66 and 7.00 for each item, indicating a high level of enjoyment. Positive feedback, such as praise for the well-designed interactions and user-friendly interfaces, was also provided.These findings suggest that it is promising to implement an interactive, physical-cognitive game-based exercise in older adults. The effectiveness of a game-based exercise program for fall risk reduction has yet to be determined. Declines in multiple physiological systems with ageing contribute to balance and gait deficits, leading to an increased risk of fall . Fall isSeveral investigators have consistently reported a strong positive effect of physical exercise on fall prevention among older adults -9. ReseaAs technology advances, a new alternative of rehabilitation approach targeting training of various physical and cognitive components in the form of interactive game-based exercises (exergames) is becoming available . These iAmong interactive game-based technology for training, the Kinect motion sensor (Microsoft Corporation) has been considered as a high-potential approach because it provides a markerless full-body 3D motion tracker and enables users to virtually interact hands-free with a computer system. Additionally, several examiners have demonstrated that among interactive game-based technology for exercising, Microsoft Kinect has an advantage in that it allows individuals to interact with games using their own body in a natural way ,44; thisBased on the usability challenges faced by older adults, programmers should develop user interfaces that are user-friendly for older adults and specific for training purposes. A previous investigator has suggested that older adults accept innovative technology when they recognize its benefit and find it meaningful for their lives .Therefore, this study focused on developing a prototype of a game-based exercise that accounts for the target user’s expectation and requirement . In particular, following a user-centered design approach, acceptability and training adherence in future empirical studies were established.The present study consists of two parts: (1) the development of a game-based exercise prototype, and (2) evaluation of target users’ feedback, response, and satisfaction regarding the developed game-based exercise prototype. The concept of development and evaluation of the target user’s experience for producing a final prototype followed a user-centered design (UCD) approach ,46,53-62In this part, the 4-phase UCD process was applied ,46,53-62The characteristics of the game-based exercise prototype are presented in A total of 5 community-dwelling older adults were enrolled as representative target users. The inclusion criteria were (1) age 65 years or older, (2) had normal cognitive function .The framework of the game-based exercise comprised 6 components , includiThe Microsoft Kinect sensor: the depth sensor that was used to track and monitor full-body movements in 3D coordinates . The tracking data were then converted to the 24 points of the Joint ID Map (body skeleton model). In this study, 4 points of the Joint ID Map  .Domain knowledge: the experts having core knowledge and experience of physical and cognitive training programs for fall prevention in older adults.Game-based training system: the digital game system, which comprised 5 games (Games I-V) with 3 levels (levels 1-3) and feedback .User and laptop computer: a system operator who was responsible for controlling the game-based training system while participants were playing the games.Graphical user interface: a form of user interface that allows participants to interact with the game-based exercise . An examA total of 5 community-dwelling older adults participated in the usability testing phase. Their mean age was 70.40 (SD 5.41) years (range 65-79 years). No participants had any experience with using exergames. They had low incidence rate of falls in the past 12 months, and they either did not take medication or took only one type. The participants’ characteristics are summarized in All 5 participants completed the game-based exercise and answered the PACES questionnaire. The average score on each item was between 6.66 and 7.00, which indicated greater levels of enjoyment. The 18-item PACES scores are illustrated in The feedback and suggestions provided by the users are presented in In this study, we aimed to develop and test the usability of a virtual, interactive game-based training system that is focused on simultaneously training the physical and cognitive function  of community-dwelling older adults. The core games were formulated by integrating the principal knowledge and existing evidence from the literature related to effective fall prevention exercise programs for older adults as well as by subjecting the games to critical appraisal from experts. We also assessed older adults’ experiences in terms of enjoyment and game features using the PACES questionnaire and a structured interview.Several intervention studies have reported high dropout rates and limited use of technology-supported platforms for delivering exercise training programs -78. InteTo our knowledge, our game-based exercise is the first game prototype that was mainly designed to support older adults in combined physical-cognitive exercising using the Microsoft Kinect motion sensor. In particular, the game-based exercise focused on the core impairment aspects that are related to falls in older adults, including balance and stepping performance as well as executive function, attention, and memory. Several investigators have consistently reported that the most important component of exercise programs for fall prevention is balance training ,7. In adThis study has certain limitations that need to be acknowledged. This study is a preliminary study that involved a small number of participants who were all female. Further, results on the enjoyment and experiences of using the game-based exercise prototype were obtained from a single training session. Thus, the findings should be considered preliminary and interpreted with caution. Future studies with larger sample sizes, a balanced gender ratio, and data obtained from multiple training sessions are warranted. Another limitation concerns the hardware specification of the Microsoft Kinect sensor. In this study, the capture volume of the Kinect sensor was restricted to 0.5 to 4.5 m, which partly limited the design of the configurations of the games. Further studies should consider using multiple Kinect sensors to cover a greater capture volume. Moreover, our game system was designed for the individual player. Exergames systems that allow group players should be considered for promoting social interaction. Finally, this study investigated the enjoyment during game engagement using the 18-item PACES questionnaire. In future studies, the 8-item version of PACES would be an appropriate questionnaire to reduce the completion time.This preliminary study demonstrated a prototype of a game-based exercise for older adults using the Microsoft Kinect sensor. The game-based exercise prototype contained combined physical and cognitive training elements with different levels of difficulty. The developed game-based exercise was well accepted by the target users, with prominent enjoyment and positive feedback. Thus, the game-based exercise appears to be a promising tool for enhancing older adults’ motivation to engage in physical-cognitive exercise with the aim to reduce the risk of falls.


The study was aimed at encapsulating the evidence of in vitro and in vivo antiviral activities of molnupiravir and its active form against highly pathogenic SARS-CoV-2, the pathogen responsible for COVID-19, and finding out the efficacy and safety of molnupiravir in clinical trials.Information on publications was explored on several databases, gray literature was reviewed, and the outcomes were discussed narratively. Molnupiravir's antiviral efficacy and associated mechanism of action have been verified in vitro against both non-COVID and multiple coronaviruses. Molnupiravir has been tried in preclinical investigations in numerous animal models against non-coronaviruses. Clinical studies in several countries are now being conducted to evaluate its antiviral efficacy in persons infected with COVID-19. The medication displays antiviral effect via generation of copying mistakes during viral RNA replication.Molnupiravir is the first oral antiviral medicine to show considerable and convincing antiviral activity in vitro and in animal models. Molnupiravir stops the spread of SARS-CoV-2 in animals that have been infected and in cells grown in a lab. In a clinical research, early molnupiravir treatment reduced hospitalization and death risk in unvaccinated individuals with COVID-19. In the battle against SARS-CoV-2, it could be a potent weapon. However, its role in COVID-19 in moderate to severe cases is still up in the air, and more research is needed. During the global pandemic of Coronavirus Disease 19 (COVID-19), which was brought on by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), there was a highly transmittable and pathogenic viral infection, that led to a theatrical loss of human life worldwide. An outbreak of the novel coronavirus affected Wuhan city, China, at the end of 2019. Within fifty days of the epidemic, over eighteen hundred individuals died and over seventy thousand were infected , blocking the formation of virus particles, or targeting a host factor involved in replication  of the synthetic nucleoside derivative N4-hydroxycytidine, which gets hydrolyzed in vivo to an intermediate EIDD-1931 (NHC or -DN4-hydoxycytidine) and is allocated to tissues, where it's transformed into an active 5'-triphosphate by host kinase  is an orally active investigational antiviral medication that was developed to treat hepatitis and influenza , exhibited antiviral activity in both the BVDV and the HCV replicon cell cultures , which require infusion during a clinical visit, suggest the benefits of molnupiravir for a broader distribution, being an orally efficacious agent and being more acceptable to the patient  for EIDD-2801 (Molnupiravir), a drug that inhibits RNA virus replication, advanced multi-center clinical trials were propelled to evaluate the efficacy of the drug. Merck & Co, on the other hand, has yet to reveal the dosages consumed and clinical pharmacokinetic data 2021). The company ran phase II and III clinical trials, involving over 600 COVID-19 patients, using a randomized strategy to administer molnupiravir 200 mg, 400 mg, 800 mg, or placebo twice daily for five days. The trials was to show if the treatment leads to long-term recovery or reduces the number of hospital admissions or deaths  that forces viruses to rapidly generate mutants, remain unaffected by the same drug. This limits the widespread use of remdesivir-like drugs and necessitates intravenous administration. But, Molnupiravir, provides the option that the drug can be taken at home rather than being restricted to hospital use only. Clinical applications of molnupiravir to quickly treat COVID-19 patients and prevent SARS-CoV-2 transmission may be expected to benefit both individual and public health.


The study’s aim was to develop a dermal equivalent scaffold that can mimic the architecture and biological performance of the human dermis. Poly ε-caprolactone (PCL) electrospun nanofiber material (ENF) was assembled with polyethylene glycol diacrylate (PEGDA), sodium alginate (SA) and type I collagen (CG1) to develop three groups of dermal equivalent scaffolds. These scaffolds were named PEGDA-PCL, SA-PCL and CG1-PCL. Scanning electron microscopy (SEM) images of cell-free scaffolds’ top and cross-sectional surface were collected and analyzed to examine internal morphology, specifically the adhesiveness of PCL fibers with the different scaffolds. Human dermal fibroblasts were cultured on each of the scaffolds. Cell viability studies including cell adhesion, cell differentiation and stress fiber production were conducted on each scaffold. Furthermore, the architectural integrity of each scaffold was verified by degradation analysis for 2 weeks by soaking each scaffold in phosphate-buffered saline (PBS) solution. Finally, we conducted rheological characteristics of each scaffold. Based on our results from the above analysis, the study concluded that CG1-PCL is best suitable for the dermal equivalent model and has potential to be used as a graft for skin repair. The skin is the largest organ in the body. It is one of the most important organs of the body due to its numerous functions. It regulates homeostasis, performs sensory functions, and protects the body from physical damages due to assault. Damaged skin results in loss of function, leading to various complications such as loss of fluid or infections . Non-heaHydrogel scaffolds can be tuned to resemble dermal tissues’ mechanical and biological properties . PolyethPolycaprolactone (PCL) is a non-toxic, biodegradable polymer with good biocompatibility. PCL is one of the ideal materials for preparing nanofibers using an electrospinning process that provides it with high biocompatibility . ElectroThe fiber material, fiber diameter, fiber alignment in the membrane and thickness of the membrane control the biological functions of the membrane . The fibn 700 mol, Sigma), Dulbecco’s phosphate buffered saline (DPBS), alpha-alpha-dimethoxy-alpha-phenylacetophenone (MW = 256.35 g/mol), and 1-vinyl-2-pyrrolidone (MW = 111.14 g/mol). These chemicals were purchased from Sigma-Aldrich, St. Louis, MO, USA. To prepare alginate hydrogels, we used sodium alginate ), Dulbecco’s Modified Eagle Medium (Thermo Fisher) and calcium chloride. To prepare CG1 hydrogels, we used Type I bovine collagen , phosphate buffered saline solution (PBS). Calcium chloride and NaOH were collected from a chemistry laboratory at the University of Central Oklahoma. To produce PCL fibers, we used PCL beads  and acetone (laboratory reagent ≥ 99.5%). Both PCL pellets and acetone were purchased from Sigma Aldrich, St. Louis, MO, USA).To prepare PEGDA hydrogels, we used polyethylene glycol diacrylate (PEGDA),  was mixed with ultrapure water and a premix concentrate of media salts, sodium bicarbonate, and fetal bovine serum as previously published ,31. FirsThis study used a Hitachi™  3000 scanning electron microscope (SEM) to observe the quality of fiber embedding and internal morphology in each group of hydrogel. SEM images were captured on the top fiber layer surface to observe fiber integration with the hydrogel, whereas cross-section images of paraffin-embedded scaffold samples were examined to observe internal void or porosity. For SEM histology sections, each scaffold was made without cells and inserted into optimal cutting temperature fluid (OCT) using tweezers. The procedure was performed carefully to prevent any air bubbles from forming in the fluid. Tissues embedded in OCT were chilled to −20 °C to harden samples for frozen sectioning using a cryostat (Leica CM1800). Thin sections of tissues were collected onto room temperature microscope slides and stored at −20 °C. SEM images were captured from all sections. 2 incubator). The cell medium consisted of DMEM/high glucose, 5% FBS and 1% penicillin/streptomycin/amphotericin  was used for all studies. Cells were extracted from the dish using 1× trypsin/EDTA solution  for 5 min at room temperature. Cell extraction was followed by serum inactivation. Cells were embedded within the collagen in CG1-PCL tissues; CG1-PCL dermal equivalents were cultured submerged for 7 days to allow cells to remodel the collagen to provide sufficient tension and stiffness for proliferation and differentiation [This study used human dermal fibroblasts  HDF 01035). Cells were cultured on tissue culture dishes in standard culture conditions  × 100%/W0. In this equation, W0 is the weight of the scaffold after 24 h of soaking in PBS and Wt is the weight of the scaffold after each time period of soaking in PBS.Each scaffold weight was measured immediately upon fabrication using a precision balance, then soaked in PBS for 24 h, 7 days and 14 days. After each period of soaking, the weight of the scaffold was measured. The value of the rate of degradation was measured using the formula: , the mean number of cells proliferated after adherence was higher for the CG1-PCL samples compared to the other samples. A large value of standard of error was found for each assay. There was no significant difference in the mean amount of adherence as measured by presence of cell nuclei  among sample groups (p > 0.05)  .p value = 0.05) as well as the PEGDA-PCL and CG1-PCL sample groups   between the PEGDA-PCL and SA-PCL sample groups ( = 0.07) . There wStress fibers successfully occurred on the surface of all types of dermal equivalents as shown in The CG1-PCL scaffold demonstrated a consistent degradation from the first week, whereas there was an increase of weight due to absorption of water for the SA-PCL scaffold during the two-week study period . The stuThere is a significant difference among the rheological properties  observed among the samples group . Figure In this study, each hydrogel-PCL was stained for cell attachment, differentiation, proliferation, and stress fiber detection analysis. This was performed to determine whether the fabricated graft maintained certain conditions necessary for wound healing. For example, cell differentiation showed that fibroblasts were present in the tissues, and more specifically the presence of stress fibers and elongate shape of cells demonstrated that cells were under mechanical tension. Presence of DAPI-stained nuclei confirmed the presence of cells in each type of hydrogel. Cell proliferation in the hydrogels demonstrated that the environment was permissive to soluble signaling by the growth factors present in the serum. All these properties indicate that each scaffold could function as an in vivo dermal graft allowing host cells to populate the tissues and specialize into dermal fibroblasts with wound-healing capabilities.This study found that PEDGA PCL nanofibers have good qualities regarding attachment of HDF cells. Cell culture assays found proliferation of cells and cell differentiation was also present. The amount of cells proliferating and differentiating was not as high as expected due to the lack of sufficient porosity on the scaffold. Physical characterization of the PEGDA PCL NFM dermal equivalent showed good absorption and degradation properties, therefore warranting further research to improve porosity in the PEGDA hydrogel. Our future goal is to micro-machine 3D PEGDA architecture and then coat PEGDA with PCL NFM to produce a PEGDA-PCL scaffold.In this study, SA hydrogels were able to integrate with PCL NFM to improve the biological performance of the SA hydrogel. The successful integration between hydrogel and nanofiber matrix was enhanced by fibroblast cell culturing on the coupled polycaprolactone (PCL) and hydrogel constructs. Cell adhesion on PCL is important because it allows for cell spreading, migration, viability, and growth . PCL is Based on the cell proliferation and differentiation assays, we conclude that CG1-PCL scaffold is the best choice among all the other test models used for this study. There are several reasons to use CG1 with PCL over others. First, the skin itself contains collagen I which is the main structural protein found in the extracellular matrix of the skin. Second, anchored collagen with and without the addition of PCL fibers permits fibroblasts to generate tension. This indicates that the dermal equivalent produced with collagen and PCL fibers could also restore senses such as touch to the affected wound area. Third, CG1-PCL has lower wettability and degradability compared to PEGDA-PCL and SA-PCL that makes it suitable for a dermal equivalent. Fourth, PCL nanofibers can provide adequate mechanical stability to CG1 for qualifying the resultant CG1-PCL as a skin graft. Finally, it is not possible to suture CG1 scaffold with skin. However, CG1-PCL graft can be sutured onto native skin during skin repair surgery. An in vivo evaluation of electrospun PCL graft for anterior cruciate ligament found that PCL graft can be sutured with native ACL and it can reconstruct the rat ACL . AdditioWhen comparing the rheological properties of PEGDA-PCL, SA-PCL and CG1-PCL to similar properties of hyaluronic acid dermal fillers , most coAccording to the author’s knowledge, this is the first study to compare the effect of combining PCL with PEGDA, SA and CG1 on their physical and biological properties. The main purpose of applying PCL NFM on each of the hydrogels was that PCL membranes could serve as a reservoir for the supplies of nutrient in each of the scaffolds for fibroblast cell growth. It is possible to attach extracellular matrix proteins with PCL nanofibers that can enhance the cell growth in a PCL NFM incorporated scaffold ,44. The The values of the biological properties of the PCL fiber-coated PEGDA in this study were different from our previous studies , likely The study was limited to investigating influence of coating PEGDA, SA, and CG-1 with PCL on the key physical, mechanical, and biological properties of composite scaffold for skin tissue engineering application. This study was unable to compare the effect of addition of PCL on PEGDA, SA and CG1 on the thermal properties. Our research group have recently conducted thermal analysis on PCL during the study of using PCL nanofiber mesh in a face mask application . We havePEDGA-PCL scaffold had good qualities regarding attachment of HDF cell to nanofibers. Physical characterization of PEGDA-PCL showed good absorption and degradation properties.SA-PCL had measurable physical and biological characteristics. Cell viability was exceptional where HDF cells satisfactorily differentiated inside the SA hydrogel.CG1-PCL had better biological and mechanical characteristics as a skin graft compared to PEGDA-PCL and SA-PCL.The objectives included determining the in vitro biological efficacy of PEGDA-PCL, SA-PCL, and CG1-PCL using human dermal fibroblasts. The study conducted cell adhesion, proliferation, and differentiation using standard cell culture and staining assays. In addition, morphological and degradation properties of each scaffold were compared. This study found the following:The translation of this study will be in vivo studies of CG1-PCL graft to verify whether CG1-PCL graft is suitable for skin repair.


Transthyretin amyloid cardiomyopathy, an often unrecognized cause of heart failure, is now treatable with a transthyretin stabilizer. It is therefore important to identify at-risk patients who can undergo targeted testing for earlier diagnosis and treatment, prior to the development of irreversible heart failure. Here we show that a random forest machine learning model can identify potential wild-type transthyretin amyloid cardiomyopathy using medical claims data. We derive a machine learning model in 1071 cases and 1071 non-amyloid heart failure controls and validate the model in three nationally representative cohorts , and a large, single-center electronic health record-based cohort . We show that the machine learning model performs well in identifying patients with cardiac amyloidosis in the derivation cohort and all four validation cohorts, thereby providing a systematic framework to increase the suspicion of transthyretin cardiac amyloidosis in patients with heart failure. Transthyretin amyloid cardiomyopathy is a treatable but often unrecognized cause of heart failure. We derived and validated a machine learning model based on medical diagnostic codes that identifies heart failure patients at risk for wild-type transthyretin amyloid cardiomyopathy. Although once thought of as untreatable and associated with very poor outcomes, both of the main forms of cardiac amyloidosis (amyloidogenic light chain and transthyretin) are now treatable with chemotherapy and tafamidis (a transthyretin [TTR] stabilizer), respectively3. In addition, several imaging modalities—including speckle-tracking echocardiography4, cardiac magnetic resonance5, and bone scintigraphy6—are now available to assist with the non-invasive diagnosis of cardiac amyloidosis, including amyloidogenic TTR cardiomyopathy (ATTR-CM)7. Nevertheless, despite the availability of these treatments and diagnostic techniques, patients with cardiac amyloidosis continue to go undiagnosed or are diagnosed late in the course of the disease due to lack of awareness and recognition of amyloidogenic light chain and ATTR-CMs.Cardiac amyloidosis, a prototypical infiltrative cardiomyopathy, is an increasingly recognized cause of heart failure (HF), particularly HF with preserved ejection fraction (HFpEF)TTR gene  or age-related misfolding of TTR (wild-type ATTR-CM). Patients with ATTR-CM, who develop symptomatic HF, have progressive decline in functional status and quality of life, and high morbidity and mortality with high rates of hospitalization and death8. Wild-type ATTR-CM appears to be more common than previously appreciated. In consecutive patients with HFpEF over the age of 60 years with a left ventricular wall thickness of 12 mm or greater screened with bone scintigraphy, the prevalence of wild-type ATTR-CM was 13%9. Furthermore, in consecutive patients undergoing transcatheter aortic valve replacement screened with bone scintigraphy, 16% had evidence of ATTR-CM10. ATTR-CM results from the dissociation of the normal tetrameric form of TTR into monomers that are prone to misfolding and aggregation, leading to TTR amyloid fibril formation and deposition in the myocardium8.ATTR-CM may occur due to a variant in the 12, lumbar spinal stenosis15, and distal biceps tendon rupture16. However, additional predisposing or associated medical conditions may yet be discovered, which improve the likelihood of prompt diagnosis. We sought to develop and validate a machine learning model based on administrative medical claims data in the electronic health record (EHR), with the goal of creating a resource to facilitate the systematic screening and identification of patients with wild-type ATTR-CM. We subsequently validated the machine learning model in four additional cohorts. Here we show that the machine learning (ML) model performed well in identifying ATTRwt-CM vs. non-amyloid HF in the derivation cohort and all four validation cohorts, thereby providing a systematic framework to increase the suspicion of potential ATTRwt-CM in patients with HF.Given the underdiagnosis and significant morbidity of ATTR-CM and availability of treatment with TTR stabilization, improved screening to enhance early diagnosis of the disease is essential, particularly for wild-type ATTR-CM for which no other means of systematic identification, such as genetic testing, is available. Clinical clues to the presence of ATTR-CM that have been described in the literature include bilateral carpal tunnel syndromeThis study comprised three parts as follows: 1) derivation (training) and testing of various supervised statistical learning models for the diagnosis of wild-type ATTR-CM in a large administrative medical claims dataset (IQVIA); (2) validation of the best-performing ATTR-CM model in additional large administrative medical claims and EHR datasets (IQVIA and Optum); and (3) testing of the final machine learning model in an EHR from a single, large healthcare system (Northwestern Medicine Enterprise Data Warehouse [NMEDW]) to observe the model’s performance. The details of each of the five cohorts  is listed in Supplementary Table  derivatiThe demographic and clinical characteristics of patients included in the five cohorts included in this study are summarized in Table Of the three statistical models tested , the Random Forest model had the highest area under the receiver operating characteristic curve (AUROC) of 0.93 (vs. 0.91 for logistic regression and 0.90 for XGBoost), as shown in Supplementary Table n = 261 cardiac amyloidosis HF cases, n = 39393 non-amyloid HF controls), the ML model had an AUROC of 0.80 , P < 0.00001, both indicative that the addition of the ML algorithm improved performance of a model that used conventional clinical predictors alone. We also tested the performance of the Random Forest cardiac amyloidosis model in the NMEDW HF cohort and found that it performed better than the wild-type ATTR-CM model, with an AUROC of 0.81 (Supplementary Table 20), resulted in high post-test probabilities of ATTR-CM, which highlight its potential clinical utility in raising suspicion for the ATTR-CM diagnosis in HF patients.In Cohort 5, the NMEDW HF external validation dataset . Supplementary Table The model output revealed several cardiac and non-cardiac clinical features that were associated with wild-type ATTR-CM. Supplementary Table Using medical claims data from a large, nationally representative database, we developed an ML prediction model for wild-type ATTR-CM, with subsequent validation and performance in multiple additional cohorts. Moreover, we identified antecedent diagnoses and phenotypes associated with wild-type ATTR-CM, some of which have been previously reported in the literature and some not previously described, such as unique combinations of cardiac and non-cardiac phenotypes that were predictive of ATTR-CM. The results of our study are important, because our predictive model, if successfully employed within the EHR of healthcare systems, could lead to targeted testing and confirmation of ATTR-CM in at-risk patients, which could lead to earlier treatment of patients with wild-type ATTR-CM  by raising the suspicion of the diagnosis in HF patients.21. Disease-modifying treatments are now available for both of the most common types of cardiac amyloidosis (ATTR and amyloidogenic light chain), and earlier treatment in the course of disease may be associated with greater treatment response22. Therefore, earlier and more systematic diagnosis of cardiac amyloidosis is imperative. This is especially true for wild-type ATTR-CM, because currently there is no specific test , which is indicative of the potential disease in order to pursue a definitive diagnosis. Furthermore, identification of wild-type ATTR-CM is important, as we now recognize that the condition is more common than previously appreciated .Cardiac amyloidosis is a specific cause of HF that is associated with high morbidity and mortality, and is often misdiagnosed as other, more common forms of HF and cardiac disease27. ML studies vary in quality and often do not include the comprehensive validation and performance testing reported here29. Our study used both an internal test set and four external cohorts for validation.ML models based on medical claims data for the prediction of diseases and phenotypes have been described in the medical literature with increasing frequency. Recent examples include ML models for opioid dependence, ankylosing spondylitis, drug-resistant epilepsy, prodromal Alzheimer’s disease, and dementiaIn our study, model performance was slightly better in cohorts generated from EHR data (Optum) than in cohorts generated from medical claims data (IQVIA). This finding is likely explained by the fact that EHR data are more comprehensive than claims data. Machine learning models typically perform best using the data they learn from, but our model performed marginally better on Optum EHR data than in the IQVIA medical claims data . When applied in a single-center EHR setting , the model performed well overall with an AUROC of 0.80 and better model performance compared with demographic factors and biomarkers alone. Furthermore, when the Random Forest cardiac amyloidosis model was applied to this cohort, it performed even better with an AUROC of 0.85, which is not surprising, because the NMEDW HF cohort cases included all patients with a cardiac amyloidosis diagnostic code (not just ATTR-CM diagnoses). We excluded blood cancers and other non-ATTR-related amyloidosis codes from our cardiac amyloidosis cases to try to minimize the potential of overlap of our prediction model with other forms of cardiac amyloidosis . Nevertheless, some patients with these other types of cardiac amyloidosis may have been included in the cases for Cohorts 3, 4, and 5. However, deriving and validating ML models for both the specific ATTR-CM diagnosis and the more general cardiac amyloidosis umbrella diagnostic code is important, because clinicians may use either term when diagnosing ATTR-CM. Given the potential overlap in the cardiac and extracardiac manifestations of the various types of cardiac amyloidosis, an ML model that predicts the presence of cardiac amyloidosis would still be clinically useful, because it would heighten clinical suspicion for the diagnosis, leading to further definitive testing, and a final diagnosis of whether cardiac amyloidosis is present or not. If present, the definitive testing would also determine the underlying type of cardiac amyloidosis.20. Using this pre-test probability, Table As shown in Table Our findings on the cardiac and non-cardiac phenotypes predictive of wild-type ATTR-CM confirm what is known about the disease from the literature and clinical experience. For cardiac phenotypes, these include pericardial effusion/pericarditis, atrial arrhythmias, cardiac conduction disorders, and abnormal serum enzymes ; for non-cardiac phenotypes, these include carpal tunnel syndrome, bone and joint disorders, neuropathies, and soft tissue disorders. We also identified combinations of phenotypes that were highly associated with wild-type ATTR-CM and found that the non-cardiac diagnoses associated with ATTR-CM, as expected, were present for several years prior to the diagnosis of ATTR-CM, thus highlighting the opportunity for earlier diagnosis. In addition to corroborating earlier findings, our analysis reveals a comprehensive landscape of the manifestations ATTR disease that predate the diagnosis of ATTR-CM by ascribing the relative significance of disease characteristics (phenotypes) and combinations thereof.ML represents an approach to automating increased identification of patients with diseases such as wild-type ATTR-CM. It is particularly useful, because it can efficiently evaluate complex interactions across multiple input predictors , a process that would be cumbersome using conventional statistical approaches. Nonetheless, as we have shown here, it is important to demonstrate that ML approaches outperform conventional statistical modeling, and that a variety of ML models are tested to determine the best-performing model. In addition to finding undiagnosed patients, ML models may help find patients earlier in their diagnostic journey and can reveal a broader picture of the signs and symptoms of the disease, allowing a deeper understanding of its pathogenesis and pathophysiology.30 and echocardiographic images31  could also be used to heighten diagnostic suspicion of cardiac amyloidosis and ATTR-CM. Nevertheless, the potential for improved early diagnosis of wild-type ATTR-CM with incorporation of these ML algorithms must be balanced with the potential cost implications and downstream effects of false positives.ML models, such as the one that we developed, can be used to calculate the probability of wild-type ATTR-CM in HF patients and elevate the index of suspicion, after which further confirmatory testing can be done, thereby enhancing diagnosis and clinical recognition of wild-type ATTR-CM, which is increasingly important given the high morbidity and mortality of wild-type ATTR-CM and the availability of tafamidis, a disease-modifying therapy. Importantly, such models can pull requisite data from the EHR and can be deployed in a fully automated manner, thus performing the initial step in disease detection on a broad scale. Further machine learning-based automated analyses of electrocardiogram tracingsThe work reported here has clinical applicability not only for wild-type ATTR-CM but also other diseases that are underdiagnosed or misdiagnosed. The analytical approach we used has broad applicability, because ICD is the international language of medical diagnoses and its ease of use in the EHR setting. Possible clinical applications range from clinical decision support via deployment at hospital EHRs as alerts for physicians or web-based risk calculators. It can also provide a systematic framework for understanding various constellations of signs and symptoms, especially for rare or under-recognized diseases.Strengths of our study include the development of a prediction model for wild-type ATTR-CM in a large medical claims database, with validation and testing in multiple additional cohorts. We also tested the performance of our model in an independent external cohort within a single healthcare system. In addition, we performed comprehensive mapping of ICD codes to phenotypes, which allowed us to investigate the associated signs and symptoms that may predict and predate the diagnosis of wild-type ATTR-CM. The ability to convert the predictive signals  from our model output into more readily understandable phenotypes with odds ratios for their association with wild-type ATTR-CM is an additional strength of our analytical approach. Although many of the identified phenotypes/features associated with wild-type ATTR-CM that we identified in the medical claims data are already well described in the literature, a strength of our ML approach is the ability to automatically identify patterns suggestive of ATTR-CM, which may not be obvious to the clinician, because they are derived using data from multiple diagnostic codes across several different disease domains and organ systems.Several limitations should be considered when interpreting the results of our study. First, our study is limited by the use of ICD codes to identify HF patients and as the gold standard for assigning cases and controls. HF, a broadly defined clinical syndrome, may be inaccurately coded in the medical record. In addition, because of the nature of the datasets used for our analysis and the fact that diagnosis codes do not capture electrocardiographic voltage or echocardiographic markers, we do not have data on these phenotypes or additional laboratory data , which could have assisted in the assignment of cases and controls. Therefore, in some of the non-amyloid HF controls, it is possible that cardiac amyloidosis is present but simply undiagnosed. However, given the low prevalence of wild-type ATTR-CM, gold-standard evaluation of cases and controls would only be feasible in highly selected cohorts and tertiary referral hospitals, which would reduce the generalizability of any derived predictive model. Second, our model was constructed on the basis of an ICD code specific to wild-type ATTR-CM. This diagnostic code is relatively new and may not be used universally; thus, biases could have been introduced by individual, institutional, or regional ICD coding behaviors that limit the applicability of our model. For this reason, we also tested the ML model on cases defined by the more general cardiac amyloidosis term and we also developed a specific ML model for the cardiac amyloidosis term. Furthermore, control patients with HF without the diagnosis could still have undiagnosed wild-type ATTR-CM. Nonetheless, we were able to validate our model in cohorts that included the more general ICD codes of cardiac amyloidosis and any misdiagnosis in our non-amyloid HF controls would have reduced our model’s performance. Any curated dataset with ATTR-CM and controls will introduce bias into the performance of our ML model; thus, we chose to apply our model to the setting in which it would be used clinically (the independent EHR testing cohort [NMEDW]). In this cohort, we did not prospectively perform tissue biopsy or bone scintigraphy for wild-type ATTR-CM and blood testing for amyloidogenic light chain amyloidosis, which would have validated these diagnoses. This type of prospective study could be done in the future as our model is applied in clinical practice. Third, AUROC may be misleading for relatively rare  diagnoses such as wild-type ATTR-CM. However, we report a wide range of model performance indices in addition to the AUROC and we include positive and negative likelihood ratios Table , which aWe have developed and validated a machine learning approach based on medical claims data, which provides a systematic framework for the screening and identification of potential wild-type ATTR-CM patients Fig. . DespiteThe primary dataset used for training and testing the model was sourced from IQVIA, Inc. , and was composed of medical claims for patients with ICD diagnosis codes for HF and amyloidosis  were included as features for training the machine learning algorithm.Supplementary Table Cardiac amyloidosis is an umbrella term that includes the diagnosis of wild-type ATTR-CM, hereditary ATTR-CM, and amyloidogenic light chain amyloidosis with cardiac involvement. Whether a patient with ATTR-CM is coded as wild-type ATTR-CM or the more general term of cardiac amyloidosis can vary based on clinical practice patterns and billing practices. For this reason, we evaluated model performance in both wild-type ATTR-CM and cardiac amyloidosis validation cohorts  and a specific code for “cardiomyopathy in diseases classified elsewhere” that tends to accompany a cardiac amyloid diagnoses were removed, to ensure no information relevant to the diagnosis of ATTR-CM or cardiac amyloidosis was “leaked” to the machine learning model. Model features were created by using a hierarchical mapping of ICD codes at the Sub Chapter (diagnosis category), Major (diagnosis name), and Short Description (diagnosis description) levels, obtained from the ICD Data R package and derived from the 2016 release of ICD-10-CM. New codes added after 2016 used the 2019 release of ICD-10-CM.17. The types and ranges of hyperparameters used for grid search were as follows: number of trees: 200, 300, 400, 500; maximum depth of tree: 5, 10, 15, 20, none; minimum samples per leaf: 2, 3, 4, 5; minimum samples split: 2, 3, 5, 7; bootstrap: true, false; and cross-validation (K fold): 3-fold.All machine learning analyses were performed in Python . We tested three supervised algorithms  on the training input dataset with a train-to-test split ratio of 80 : 20 using the aforementioned dataset (Cohort 1), with each patient labeled as wild-type ATTR-CM ICD-10 code (E85.82) present or absent (non-amyloid HF control). Within the training set, fivefold cross-validation was used to select hyperparameters. A grid search algorithm was implemented to determine the best parameters for each of the three algorithms. Grid search uses heuristic methods to estimate the optimal set of hyperparameters for machine learning algorithmsTesting was done in the 20% holdout sample and the following parameters were calculated based on the number of true positives, true negatives, false positives, and false negatives: sensitivity, specificity, positive and negative predictive values, and accuracy. The Random Forest model, which had the best diagnostic performance , was carried forward for further validation, as described below. In separate analyses, we repeated the aforementioned process to derive a model of the more general diagnosis of cardiac amyloidosis as opposed to wild-type ATTR-CM (see the The model derived from the IQVIA wild-type ATTR-CM population (Cohort 1) was validated in three other cohorts  was performed in accordance with the Declaration of Helsinki. The NMEDW dataset was queried for all patients with any ICD-9 or ICD-10 HF diagnosis code using a date range of 1 June 2009 to 31 May 2019. Only patients aged ≥50 years were included. Cases were patients with an organ-limited amyloidosis code (E85.4) plus a HF diagnosis code but not blood cancer, light chain amyloidosis, end-stage renal disease, cerebral amyloid angiopathy, or intracranial hemorrhage diagnoses. Non-amyloid HF controls were patients with a HF code but none of the amyloidosis codes. These were the same definitions used in Cohorts 3 and 4, with the exception that there was no matching of cases and controls . Supplementary Fig. To further examine clinical generalizability of the ML model, we performed additional external testing of the derived and validated wild-type ATTR-CM Random Forest machine learning model in the NMEDW EHR cohort19. ICD codes mapped to multiple phenotypes were de-duplicated via selection of the single most clinically relevant phenotype.Once we identified features (ICD codes) that were predictive of the wild-type ATTR-CM diagnosis, we sought to better understand the clinical meaning of these features. Therefore, we used a phenotype grouping system by combining one or more related ICD codes into distinct diseases or traits (based on PheWAS Phecode v.1.2)p-value < 0.05 after Bonferroni correction was considered statistically significant.Of the features (ICD codes) included in the Random Forest model, those that were found to have some level of variable importance for the diagnosis of wild-type ATTR-CM (>97%) were mapped to phenotypes based on PheWAS mapping described above. Next, we tested the associations of each of these phenotypes with the wild-type ATTR-CM diagnosis in Cohort 1. We used logistic regression analyses to compare individual phenotypes between wild-type ATTR-CM cases and non-amyloid HF controls, and odds ratios  were calculated. Correction for multiple tests was performed using the Bonferroni method. A two-sided Next we examined combinations of phenotypes that are associated with wild-type ATTR-CM. The Random Forest model derives its predictive power from specific interactions (trees) of features associated with wild-type ATTR-CM. We sought to elucidate these trees by identifying combinations of phenotypes with high predictive value. This was done by selecting ten phenotypes with the highest odds ratios and deriving all combinations of up to five phenotypes. We used the Python Scikit-learn set cover algorithm to identify the top phenotypes or phenotype combinations with odds ratios > 5 for further interrogation for their ability to identify the maximum number of ATTR-CM patients within the IQVIA wild-type ATTR-CM cohort (Cohort 1).Finally, we mapped the time course for the most common phenotypes in relation to the first documented diagnosis of wild-type ATTR-CM, to help delineate the temporal aspect of features in relation to the documented diagnosis of ATTR-CM.Further information on research design is available in the Supplementary InformationPeer Review FileReporting Summary


Maternal mental health is linked to early childhood development; yet there is a gap in evidence-based interventions for low-resource settings. This study estimates the impact of ‘Integrated Mothers and Babies Course and Early Childhood Development’ (iMBC/ECD), a cognitive-behavioral, group-based intervention, on maternal depression and early childhood social-emotional development in Siaya County, Kenya.This quasi-experimental study enrolled 417 pregnant women and mothers of children under age 2 across two sub-counties in Siaya County. The intervention area had 193 women in 23 groups implementing iMBC/ECD and the control area had 224 women in 30 groups exposed to ECD only content. Mother/index child dyads were followed for two years. To estimate the causal treatment effect from the non-randomized design, we implemented the propensity score weighting method with inverse probability weights.At baseline, 10.2% of participants endorsed moderate/severe depressive symptoms. At 14-months post-intervention, 7.4% endorsed moderate/severe depression. Overall, iMBC/ECD intervention did not have a significant impact on reducing maternal depression or improving children’s social and emotional development. However, sub-group analyses revealed that iMBC/ECD was associated with lowered depressive symptoms among women with no/low education, four or more children and/or no experience of intimate partner violence in the past year. Women with high program attendance  also experienced consistently fewer depressive symptoms compared to those with lower attendance.Non-randomized study, sub-group analyses are exploratory.The iMBC/ECD program may have the potential to improve maternal mental health and early child development for more targeted vulnerable populations. In LMICs, the average prevalence of depression is about 16% in the antenatal period and around 20%i in the postnatal period . The mouIn Sub-Saharan Africa, 66% of under-5 children are at risk of not reaching their full developmental potential which includes cognitive, socio-emotional, behavioral and educational outcomes . This isThe Government of Kenya has endorsed the Nurturing Care Framework and it is now included in a national policy document for community health . In WestIntegrated Mothers and Babies Course & Early Childhood Development (iMBC/ECD) program was implemented by lay counselors in Siaya County, Kenya with the intent of improving maternal mental health and the socio-emotional development of young children. The program was based on content inclusive of cognitive behavioral therapy components and ECD messages. The primary objective of this study is to determine to what extent did the iMBC/ECD intervention implemented by community health volunteers affect maternal depression for mothers of young children. The secondary objective of the study is to determine to what extent did the iMBC/ECD intervention affect the social-emotional development of young children.An intervention called the The findings of this study are expected to offer new insights on whether a community-based prevention model delivered by lay counselors in a low-resource setting can produce sustained effects on maternal mental health and children’s social and emotional development.2.In 2018, Catholic Relief Services (CRS) implemented the Integrated Mothers and Babies Course and Early Childhood Development program (iMBC/ECD)  within aThe primary purpose of the curriculum-based Integrated Mothers and Babies Course (iMBC) is to reduce the symptoms and incidence of perinatal depression among vulnerable populations . The thecare groups which were supported by CRS in the study areas. Care groups bring together women in communities via a network of community volunteers to deliver program content and are designed to be adopted by communities to produce sustainable change beyond donor funding cycles .For the intervention areas, seven government community health volunteers (CHVs) delivered the iMBC/ECD content across a total of 23 care groups and monitored uptake of learned behaviors through home visits. The CHVs were supervised weekly by two Catholic sisters (nuns) and two CRS case managers who were trained as iMBC/ECD ‘master trainers’ and who monitored the CHVs performance using fidelity checklists. The CHVs were trained on iMBC/ECD prior to the intervention starting but master trainers also offered monthly mock sessions of iMBC/ECD For the control areas, there were 30 care groups and each group had one “lead mother” who was responsible for group logistics and delivering general ECD content aligning with nurturing care components . Lead mothers conducted home visits to reinforce education messages and reported group and home visit attendance to their local CHV. CHVs conducted monthly supervision visits tailored to the challenges faced by lead mothers while supporting the group participants.2.1.This quasi-experimental longitudinal study followed mother/baby dyads for nearly two years and utilized the existing care group model under SCORE-ECD for identifying the intervention and control groups. Assignment to intervention or control was by sub-county and not randomized due to issues of program logistics.The intervention groups received iMBC/ECD content implemented over 14 in-person, group-based sessions spanning a period of 7 months (meetings occurring every two weeks). After completing the formal sessions, five iMBC/ECD follow-up booster sessions were conducted over the span of the next six months for a refresher on lessons learned during the program and to aid in practicing learned skills. The control groups received the same early childhood development education content during regular biweekly care group meetings as a part of the ongoing SCORE-ECD program. For both the intervention and control groups, care groups continued to meet beyond the duration of the study.Women aged 16 years or older who planned to stay in the study area for the duration of the program and who were either pregnant or had a child under the age of two years old were recruited. At baseline, 417 women were enrolled and interviewed: 193 women in 23 intervention groups (Rarieda sub-county) and 224 women in 30 control groups (Ugunja sub-county).Data were collected at four time points: baseline (August 2018), immediate post-intervention , 8-months post-intervention  and 14-months post-intervention . The year-long time lapse between baseline and the immediate post-intervention data collection was due to slight logistical delays in organizing data collection both before and after the iMBC/ECD program which ran from October 2018 to June 2019. Interviews lasted 60–90 min and were conducted in Luo by trained research assistants at locations conducive to confidential conversations in the community. Research assistants were also trained to provide referrals to participants who either endorsed suicidal ideation or self-harm and/or who reported experience of physical or sexual intimate partner violence in the past year. Participants had the option to accept or decline referrals. The last data collection period adhered to COVID-19 safety protocols per the IRB. The CommCare platform, published by Dimagi, Inc., was used to collect, store, and manage the study data.To ensure implementation fidelity, program process data were collected and closely monitored throughout the duration of the program. For each of the 14 formal iMBC/ECD sessions, content coverage, time management, application of lessons learned, comprehension of the materials and level of participation were assessed. On a scale of 1 to 10, the master trainers rated observed selected sessions based on their perception of how well these five criteria were performed by the CHVs. Apart from the first three sessions, the remaining 11 sessions were consistently scored above 8 out of 10 for all five of the fidelity criteria used for this study (program data not shown).2.2.Based on prior CRS programmatic data, we assumed that at pre-intervention, 15% of participants would meet the threshold for moderate/severe depression (PHQ-9 ≥ 10) in both groups, and at post-intervention, 5% of participants in the iMBC/ECD group would meet the threshold for moderate/severe depression (PHQ-9 ≥ 10) versus 15% in the ECD only control group. Using the SPA-ML program , we comp2.3.Maternal depression was assessed using the Patient Health Questionnaire (PHQ-9) which has been previously validated in Kenya . The PHQChildren’s social and emotional development was measured using the Ages and Stages Questionnaires: Social-Emotional, Second Edition (ASQ:SE-2) . The ASQ2.4.Socio-demographic and health variables were examined as baseline characteristics and/or covariates in the analysis. Variables included maternal age, education, household size, parity, self-reported hope via the Herth Hope Index , the tot2.5.STROBE guidelines were followed for analysis and presentation of results. A STROBE flow chart  shows paa priori . The primary maternal outcome is PHQ-9 score at 14-month post-intervention follow-up (follow-up 3). The primary child outcome is the mean ASQ-SE item score also at follow-up 3. The mean item score was chosen to make the ASQ-SE comparable across different child age groups. To estimate the causal treatment effect from the non-randomized design, we implemented the propensity score (PS) weighting method to control for potential confounding factors. We estimated the PS using CBPS package in R . Potentiiori see . Both iniori see , and IPWThere was sufficient overlap of the distributions of estimated PSs in both the intervention and control arms, with the majority of the PSs distributed in the middle range (data not shown). The IPWs derived from the estimated PSs successfully reduced covariate imbalance . As for Continuous primary outcomes were analyzed using linear mixed effects models so all comparisons of interest could be estimated from the same model. The identity link was used to estimate differences in mean outcomes. All mixed models included “care group” as a random intercept to account for the clustered study design. We also included a random personal-level intercept and random personal-level time slopes to account for the correlation of outcomes within person across time. We used maximum likelihood (ML) estimation, weighted by the pre-specified IPWs, with robust variance estimators to take weighting into account . Interviews were conducted in Luo or English, which-ever was preferred. A designated note taker accompanied the interviewers for extensive notetaking during the interview (no audio-recordings). Rapid content analysis was conducted using an analysis matrix in Microsoft Excel.Ethical approval was received from the Duke University Campus IRB (# 2018–0595) and the Maseno University Ethics Review Committee in Kenya (# MUERC 00587/18). All participants provided written informed consent, or, if they were illiterate, were read the consent form and provided their fingerprint with a witness signature.3.3.1.There were 417 participants in 53 groups at baseline . Of thes3.2.There were no significant effects  of the intervention on mean PHQ-9 scores. At immediate post-intervention follow-up, we observed a slightly larger but non-significant decrease in mean PHQ-9 score  from baseline in the treatment arm than in the control arm [estimate (95% CI): −0.6 ]. At 8-month post-intervention, the difference in improvement from baseline was attenuated [estimate (95% CI): 0.1 ]. However, at 14-month post-intervention, we observed a larger but still non-significant decrease in mean PHQ-9 score from baseline in the intervention arm than in the control arm again [estimate (95% CI): −0.9 ]. See 3.3.There were no large effects of the intervention on mean ASQ-SE item scores. The average ASQ-SE mean item score at 8-month post-intervention was slightly higher in the intervention arm [estimate (95% CI): 0.2 ]. However, the average ASQ-SE mean item score at 14-month post-intervention was slightly higher in the control arm [estimate (95% CI): −0.2 ]. See Examining the individual ASQ-SE items descriptively, participants had the greatest concerns with issues of breastfeeding, soothing the child and diarrhea for infants under 12 months. Between 12 months and 48 months, participants’ greatest concerns were with children hurting themselves, hurting other children, adults, and animals, damaging things on purpose, excessive crying, repeating the same behavior and getting upset when trying to stop the child. Problems with eating was also a reported concern.Unweighted regression results are similar to the estimated effects from the primary analyses (data not shown).3.4.In the analyses examining the effect of attendance in the intervention arm, in most cases this was not significant  See –S6. The 3.5.Effect modification analyses revealed that at the immediate post-intervention follow-up, the iMBC/ECD intervention was associated with: lower PHQ-9 scores (i.e. fewer symptoms of depression) for women with no education or only primary education [estimate (95% CI): −1.54 ]; lower PHQ-9 scores for women with four or more living children [estimate (95% CI): −2.75 ]; and higher ASQ-SE mean item scores  overall and also for multiple sub-groups of women including younger-aged mothers and mothers who have experienced emotional intimate partner violence in the past year.At the 14-month follow-up period, the iMBC/ECD intervention was associated with lower PHQ-9 scores for women who have not had any experience of physical or sexual intimate partner violence in the past year [estimate (95% CI):−1.93 ] or have not experienced controlling behavior by husbands/partners in the past year [estimate (95% CI): −2.28 ]. The iMBC/ECD intervention was also associated with higher ASQ-SE mean item scores overall. See 3.6.Almost all iMBC/ECD participants mentioned having had a positive experience, particularly regarding the opportunity to share with other mothers about their life circumstances and using the contextualized storytelling method. Some participants mentioned that communication with their husbands/partners had improved and the majority of the participants also indicated that they were strongly supportive of husbands/partners involvement in some version of the iMBC/ECD program, including discussions about couple’s relationship dynamics and ECD. With regards to iMBC program content specifically, some leaders indicated that participants expressed difficulty implementing some cognitive-behavior therapy practices such as identifying and defining thought patterns and practicing “thought interruption”, where one replaces a negative thought pattern with a positive thought. Participants seemed to have more traction with understanding and practicing lessons related to pleasant activities and planning day-to-day activities. Participants also expressed that the iMBC/ECD program could benefit from including economic strengthening activities.4.Findings of this evaluation provide valuable programmatic insights for future community-based, integrated caregiver mental health and ECD interventions in low-resource settings. Overall, the iMBC/ECD intervention did not have a significant impact on decreasing maternal depression symptoms or on increasing the socio-emotional development of children compared to the ECD only control group. It is important to note however that the study did not specifically target women with depression. Rather, this was a community-based, prevention-oriented intervention, hence the sample had relatively low mean PHQ-9 scores at all time points. This was true even with the occurrence of the COVID-19 pandemic before the last data collection time point in August 2020. Moreover, evaluations of prevention-oriented mental health interventions are still nascent in low-resource settings and further studies are needed to understand their impact and potential .The lack of measured effect begs the question of whether iMBC was appropriate for the context. The largely picture-based iMBC program was steeped in cognitive behavioral therapy (CBT) and the literature regarding the efficacy of CBT for a range of specific mental and behavioral problems is strong, although largely based on studies in high-income countries . That saDespite the program including participant role play and using real-life scenarios tailored for Kenya via storytelling, participants’ difficulty with components of CBT exercises such as ‘though interruption’ may have affected overall program effectiveness. The program team performed internal checks (i.e. sister master trainers using fidelity forms) to ensure that the facilitators implemented each program session as intended; however, it is still plausible that there was a disconnect between lay community health volunteers (CHVs) delivering iMBC content with ‘fidelity’ and participants receiving in the information in a way that changed their feelings, thoughts, and/or behaviors. Likewise, general challenges with task-shifting have been reported in other settings  and therAnother reason for the lack of an overall program impact may have been because all study participants were part of care groups and these groups, regardless of content, had an unexpected positive effect on mental health. It is plausible that just being a part of a regular support group with other women in similar life stages and having a sense of belonging may positively influence their mental health. However, this is largely speculation and we call for future studies to investigate this topic further.Program attendance mattered for program impact. Women with high program attendance  experienced consistently fewer depression symptoms compared to those with low program attendance . This finding is consistent with a recent review outlining programmatic guidance for ECD interventions in high HIV burden countries . The revRegarding the intervention’s lack of desired impact on children’s socio-emotional development (i.e. ASQ-SE mean item scores were slightly higher in the intervention group compared to the control group), the findings must be interpreted in the context that the mean ASQ-SE item scores were generally low at all follow-up time points in the study  and the difference in the ASQ-SE mean item scores between the intervention and control groups at the follow-up time points were miniscule. This perhaps signified that there was little room for developmental improvement in the children of our study sample by the intervention. It is also important to note that ASQ-SE was initially designed for screening purposes and not necessarily for longitudinal research. Regardless, we generally recommend that an integrated ECD curriculum should include multiple didactic and interactive practice sessions that focus on age-appropriate responsive caregiving and feeding, early stimulation and positive discipline.In order for future programs to potentially have broader community-wide impact, the existing iMBC/ECD curriculum may need to be integrated with components that more directly address the socioeconomic determinants of mental health at the individual, familial and/or community levels . For exaIn addition, intimate partner violence cannot be overlooked in community-based mental health and early child development programming. We found that iMBC/ECD, relative to the control group, was associated with lowered depressive symptoms among women who have not had any experience of physical or sexual intimate partner violence or marital control in the past year. This suggests that without proactively addressing intimate partner violence, cognitive behavior therapy alone may not be enough when persistent mental health problems are primarily driven by difficult relational dynamics and abuses that are beyond the women’s control. As such, involving male partners, with the women’s consent, in community-based intervention models could be a key strategy moving forward. Past studies have highlighted the value of male involvement in improving maternal mental health and early child development interventions . AnotherThere have not been many interventions designed to address both caregiver mental health and ECD in an integrated manner in low-resource settings . The curThere are study limitations. First, this was not a cluster-randomized controlled trial. Rather, two sub-counties were purposefully selected and the iMBC/ECD program was rolled out in one of the counties. Despite this limitation, application of the IPWs using covariate balancing PSs created a weighted target population in which women in the intervention and control groups were comparable, at least on a set of observed covariates included in the estimation of the PSs. Second, even though balance was sufficiently achieved on a set of important observed covariates, the control group may not have been a true control to the intervention group. Third, this study was likely underpowered to assess the program’s effect on maternal mental health and children’s social and emotional development because initial expectations were that the level of moderate/severe depression (PHQ-9 ≥ 10) would be higher than they actually were. That said, there were no noticeable practical differences in depression scores that would have influenced our conclusions. Findings related to sub-group analyses (effect modification) and supplemental analyses comparing women with high versus low attendance are also exploratory and not powered. Nonetheless, we believe that these additional analyses provide helpful programmatic insights and nuanced understandings of the main findings.5.Overall, the iMBC/ECD program did not have a measurable impact on our primary outcomes at a population level; however, we note that this study was in a context where the majority of participants did not show indications of moderate to severe depression and their children had favorable developmental scores from the onset. With this in mind, the program may have the potential to improve maternal mental health and early child development for more targeted vulnerable populations  and under specific conditions . Going forward, programs with the dual purpose of improving maternal mental health and child development and their associated evaluations should consider the implications of a non-targeted, community-level approach versus a more targeted approach that may optimize impact. Likewise, these integrated programs should also consider including components that address physical or sexual intimate partner violence or marital control and the socioeconomic determinants of mental health. Further research is still needed to identify effective, integrated programming in support of responsive caregiving in order to ensure that all mothers and their children thrive.1