Since its first case of COVID-19 on March 13, 2020, Ethiopia has exerted efforts to curb the spread of SARS-CoV-2 (COVID-19) without imposing a nationwide lockdown. Globally, COVID-19 related disruptions and mitigation measures have impacted livelihoods and food systems, nutrition, as well as access and use of health services.To develop a comprehensive understanding of the impacts of the COVID-19 pandemic on food systems, health services, and maternal and child nutrition and to synthesize lessons from policy responses to the COVID-19 pandemic in Ethiopia.We conducted a review of literature and 8 key informant interviews across government agencies, donors, and non-governmental organizations (NGOs), to map the impacts of the COVID-19 pandemic on the food and health systems in Ethiopia. We summarized policy responses and identified recommendations for future actions related to the COVID-19 pandemic and other future emergencies.The impacts of the COVID-19 pandemic were felt across the food system and include limited agriculture inputs due to travel restrictions and closed borders restricting trade, reduced in-person support by agriculture extension workers, income losses, increases in food prices, and the reduction in food security and dietary diversity. Maternal and child health services were disrupted due to fear of contacting COVID-19, diversion of resources, and lack of personal protective equipment. Disruptions eased over time due to the expansion of social protection through the Productive Safety Net Program, and the increased outreach and home service provision by the health extension workers.Ethiopia experienced disruptions to food systems and maternal and child nutrition services due to the COVID-19 pandemic. However, by expanding existing social protection programs and public health infrastructure and leveraging partnerships with non-state actors, the extent of the impact of the pandemic was largely minimized. Nevertheless, vulnerabilities and gaps remain and there is a need for a long-term strategy that considers the potential for future pandemics and other shocks. Coronavirus disease COVID-19), caused by infection with SARS-CoV-2, has led to a global public health crisis. The earliest cases were detected in December 2019, and on March 11, 2020, the World Health Organization (WHO) characterized COVID-19 as a pandemic 9, caused. AdditioEthiopia began implementing measures to address the COVID-19 pandemic in January 2020, ahead of most countries and before it detected its first case, by introducing strict passenger-screening protocols at airports and conducting house-to-house screenings for 11 million households . The govGlobally COVID-19 disruptions to the food systems have varied by context and based on the extent of government lockdowns, and have included impacts on the availability of inputs for agriculture and food supply chains, food prices, and consumers’ income . These fHealth systems have also been strained due to the COVID-19 pandemic, which may have profound impacts on maternal and child health . EthiopiWe conducted a literature review and key informant interviews to develop a comprehensive understanding of the impacts of the COVID-19 pandemic on food security, nutrition, and health in Ethiopia, and assess programs and policies responses to mitigate these impacts. Our objectives were to: 1) examine how the COVID-19 pandemic has influenced the food system in Ethiopia including its effects on agricultural production, food prices, and consumer behaviors; 2) assess the impacts of COVID-19 on health systems and access to maternal, newborn, and child health and nutrition services.Ethiopia is the second-most populous nation on the continent with a population of 115 million people . ApproxiWe collected data through 1) an extensive literature review including a review of published and grey literature, and 2) key informant interviews with stakeholders in Ethiopia. Data was collected between November 2020 and July 2021.We used Google Scholar and PubMed to search for and obtain literature on the impact of the COVID-19 pandemic on the food and health systems in Ethiopia. The research team also reviewed grey literature from stakeholders such as UNICEF, International Food Policy Research Institute (IFPRI), Ethiopia Ministry of Health, and the World Bank including reports, briefs, and webinars. The research team identified work to be reviewed through an independent search and based on recommendations from key informants.The research team identified potential interviewees from a list of authors of recent reports on COVID-19 in Ethiopia, through contacts in research institutions, universities, and local non-governmental organizations. We recruited informants through snowball sampling by which contacts referred us to experts with knowledge on this topic. Due to travel restrictions, the research team conducted virtual expert interviews with eight stakeholders based in Ethiopia and the United States. Interviews were conducted from January to July 2021. The full list of interviewees may be found in the Acknowledgments section.A semi-structured questionnaire was used to conduct interviews. Interviews were conducted by three members of the research team  (Appendix 2). During each interview, the research team asked interviewees a series of questions about the impacts of COVID-19 on the food and health systems and the policies implemented to address these disruptions. At each interview, interviewers took notes which were consolidated following the conclusion of the interview.Ethical approval for the study was sought from the Institutional Review Board (IRB) at the Harvard T.H. Chan School of Public Health, which determined that this work is not human subjects research as defined by the regulations of the U.S. Department of Health and Human Services (DHHS) and Food and Drug Administration (FDA).We extracted three common themes from the findings: (a) impacts of the COVID-19 pandemic on the food system ; (b) impThe key informant interviews and the literature review outlined possible areas of impact for COVID-19 on collaborative agriculture activities and access to agricultural imports. Key informants highlighted that the impact of COVID–19 on agriculture production would be felt mostly in rural areas, as agriculture is the main form of livelihood. However, at the time of the interviews in January 2021, respondents noted that the rural areas in Ethiopia had few cases of COVID-19 and related disruptions were not likely to be extensive. Nevertheless, one respondent stated the collaborative group work of agriculture has the potential to spread the virus if social distancing is not enforced during such activities. By July 2021, another key informant indicated that farmers had concerns about access to agriculture inputs such as fertilizer because of international price increases. However, the informant noted that the impacts of COVID-19 on these price changes cannot be disentangled from other policies such as changing exchange rates.Studies examining the impacts of COVID-19 on agriculture activities in Ethiopia reported similar impacts. One study found that within the agricultural sector, field-level agricultural activities that supported social distancing were least affected, however, support from Agricultural Extension Workers and access to input activities were most affected . The mosAlthough there was no full nationwide lockdown, mobility in parts of Ethiopia was restricted due to disruptions to public transportation. One of the key informants reported that while there was tension among the public, there was not much impact on the food distribution system because there were no restrictions in food transportation. Another key informant similarly observed that food availability for consumer purchase was largely unimpacted after the onset of the COVID-19 pandemic. Key informants highlighted that the impacts of COVID-19 on food security and consumption were not as profound as initially anticipated at the start of the pandemic.While some surveys reported increases in food prices, the literature review supported the key informants’ reports of minimal changes to dietary consumption because of the COVID-19 pandemic. Phone surveys carried out in Addis Ababa and Kersa documented reported changes in food prices during the COVID-19 pandemic. A cross-sectional phone survey in Kersa with 297 respondents from July to August found that over 95% of households reported that food prices increased from the start of COVID-19 for staples , pulses , fruits, vegetables and animal source foods [Similarly, the Ethiopian Public Health Institute and World Food Program reported that the cost of a healthy diet increased by about 17% in Amhara and 16% in Oromia between January and October 2020 . A vegetKey informants indicated that many workers particularly in urban areas were unable to operate at full capacity because of the risk of transmission of COVID-19. Similarly, a phone study by IFPRI in Addis Ababa found that 64% of respondents reported a decrease in income relative to their standard income in July 2020 . Loss ofThe impacts of COVID-19 on access to foods were most pronounced in the early months of the pandemic and decreased over time. Telephone surveys conducted by the World Bank in urban and rural households across all regions of Ethiopia found that while respondents were unable to buy certain foods due to market closures in April 2020, these disruptions subsided by May 2020 .A few studies examined how food security and dietary consumption changed throughout the COVID-19 pandemic. The survey conducted in Addis Ababa from August to September found that 54.2%, 19.9%, and 15.3% of households reported worrying about running out of food, skipping a meal, and going for a whole day without eating in the past month, respectively . HoweverThe Ethiopian government has mainly relied on the country’s existing prevention-based primary public healthcare and health extension systems for healthcare provision. The former consists of 17,000 health posts and 3,700 health centers which employ three out of four Ethiopian healthcare professionals . The COVDifferences in the effect of the pandemic on key maternal and child health and nutrition services were observed between PHCUs  in model woredas, which have successfully implemented a community health extension program and other national social mobilization strategies, and non-model woredas . PHCUs iCOVID-19 negatively impacted the provision of key maternal nutrition services delivered at health centers, at the community level, and through schools. For services provided at health centers including maternal nutrition screening, iron, and folic acid (IFA) supplementation and antenatal care (ANC), the disruptions were more pronounced during the first two months of the lockdown (March and April), and gradually started improving afterward . A studyPhone surveys with HEW in rural areas similarly demonstrated a decrease in time spent on key maternal services as time spent on COVID-related activities increased . In May For services provided at the community level through health posts, disruptions due to the COVID-19 pandemic were most pronounced for activities that require close contact between individuals such as prenatal counselling for women, maternal deworming programs, and complimentary food preparation demonstrations . These sTwo interviewees highlighted that the health system was not prepared to handle challenges in treating malnutrition cases among children at the beginning of the pandemic. Respondents noted that while there were no shortage of supplies or limitations in the availability of services to treat malnutrition, fear of contracting the virus translated into decreased demand for these services. According to an interviewee, approximately half a million children received malnutrition treatment pre-COVID, through the network of 20,000 PHCUs in Ethiopia . Most ruIn Ethiopia, most severe acute malnutrition (SAM) cases are treated using an outpatient treatment program that uses ready-to-use therapeutic foods (RUTF). Prior to the COVID-19 pandemic, approximately 50% of RUTF for SAM treatment in Ethiopia was imported and the other half was supplied through HILINA Enriched Foods complex, a local producer. As key stakeholders were concerned about the potential disruption of the product supply from abroad during the pandemic, they shifted their strategy whereby HILINA started supplying all required RUTF. This strategy allowed treatment centers to continue to have sufficient resources for SAM treatment in the selected targeted districts. Despite this effort, treatment for SAM decreased by more than 50% compared to 2019 overall .However, another informant indicated that although COVID-19 posed challenges in the delivery of other child health and nutrition services provided in the health centers, such as vitamin A supplementation, under five years old (U5) child deworming, and screening for malnutrition increased in 2020 compared to 2019 . ServiceKey policy actions and the multitude of strategies were undertaken by the Ethiopian government and implementation partners to mitigate the impacts of COVID-19 on the food and health systems are shown in Ethiopia launched a nationwide public information campaign and the Community Based Action and testing (ComBAT) campaign . The latDuring the pandemic, to secure resources and formulate decisions in an agile manner, Ethiopia has benefitted from strong coordination between stakeholders from the public sector, civil society, and international organizations. International organizations such as IFPRI and UNICEF, responded to the pandemic by conducting data collection and analyses to understand the impacts of the pandemic and inform policy responses. Key informants interviewed in this study noted that civil society organizations have played a major role in communicating public health messages. In the health sector, respondents noted that multi-sectoral forums conducted through conference call or instant messaging applications allowed for coordination to continue despite social restrictions.We conducted a literature review to determine the impacts of the COVID-19 pandemic on the food and health systems and evaluate the policies that were implemented to mitigate the impacts of COVID-19 in Ethiopia. We found some evidence of disruption of food system across multiple entry points including agricultural production, food availability and affordability, and dietary intake. Similarly, we found that COVID-19 related disruptions to the health systems and related nutrition services were present in the first few months. In the food system these included disruptions to the input supply due to travel restrictions and closed borders restricting trade, reduced in-person support by agriculture extension workers, income losses, increases in food prices, and the reduction in food security and consumption of less diverse diets. The implementation of innovative strategies and the leveraging of existing support services, such as the PSNP, reduced these impacts. The provision of nutrition services through health centers decreased in the early stages of the pandemic, primarily due to fear of contracting the virus. Impacts on health systems were mitigated over time as fear surrounding the virus subsided, PPE became available, and strategies were implemented to adapt to the disruptions, such as outreach and home services provided by the health extension workers, identification of most rural woredas and connecting them with the necessary resources, and the change to a local supplier for ready-to-use therapeutic foods .The impacts of COVID-19 on the health and food systems mirror many of the impacts felt around the world, particularly in sub-Saharan Africa (SSA). Within the food system, surveys conducted in Burkina Faso, Nigeria, and Zimbabwe found that, as in Ethiopia, the COVID-19 pandemic was associated with food price increases, reduced food availability, and lower dietary diversity 19. A studyThe disruptions to nutrition services we report are also in line with similarly reported disruptions in SSA. A multi-country study, assessing the effects of the pandemic on healthcare services found that more than half (56%) of essential health services, including maternal and child services, were affected by the COVID-19 pandemic . These dTo mitigate these impacts, governments across sub-Saharan Africa implemented macroeconomic and sector-specific policies that reflected aspects of Ethiopia’s response. For example, social protection programs were substantially scaled up across the region, with cash transfers increasing from 3% right before the pandemic to 11% in July 2020 . Africa’The COVID-19 pandemic also underlines the fragility of Ethiopia’s social protection and healthcare systems. An inherent weakness of the systems is their reliance on external funding from organizations such as the World Bank and USAID, which means Ethiopia is exposed to shocks elsewhere. This vulnerability calls for the need for diversifying funding sources and developing local tax systems to fund social protection programs and healthcare service provisions in the long term.There are several limitations to our study. First, considering that the COVID-19 pandemic is still ongoing, many of the impacts are yet to be seen and felt. Ongoing research and future reports will inform future practices to guide emergency responses. Second, our sample size of key informants was relatively small and therefore our results may be missing the insights from other relevant stakeholders. Third, when assessing impacts of the pandemic, we face difficulties disentangling them from the effects of other shocks experienced by Ethiopia, including conflicts in different parts of the country, macroeconomic instability due to a lack of access to foreign currencies, as well as natural disasters, such as locust infestations and flooding. As the pandemic continues, Ethiopia needs to maintain policy vigilance and invest in a long-term strategy that considers the cyclical nature of COVID-19 cases even when the number of cases subsides.Ethiopia has been able to respond to the impacts of the COVID-19 pandemic on the food and health systems by leveraging existing social protection and public health infrastructure. This pandemic has been an exercise for Ethiopia and a lesson for future pandemics, especially in terms of resource harnessing and mobilization, capacity building and public guidance. Besides being more prepared to tackle other pandemics in the future, Ethiopia’s successful response, as described by respondents, can set the right path to dealing with other infectious diseases that claim the lives of more locals, such as tuberculosis. Working in agile environments, pursuing actions, assessing resources, setting emergency plans, and securing the population’s needs, especially in terms of nutrition, constitutes key actions to respond to future pandemics or outbreaks.


Grains are widely consumed all over the world, providing calories, macronutrients, micronutrients, dietary fiber, minerals, and plenty of phytochemicals . These nRecently, high-throughput omics techniques have enabled the comprehensive understanding of the biological perturbations that are associated with dietary intake and other environmental exposures ,13. AltePrevotella, was able to predict weight loss in a 6-week whole-grain intervention [The precise prediction of individual responses to nutritional interventions could function as fundamental scientific evidence towards the practice of precision nutrition in clinical and community settings. For example, various genotypes were found to interact with different carbohydrate intake levels in relation to weight/fat loss and other cardiovascular traits in the POUNDs Lost trial . We alsorvention . ClearlyThe majority of the world’s population consume a large amount of grains every year, which contributes to the greatest proportion of calories in our diet. There is a requirement for more detailed research outcomes on the health benefits of different grains and their underlying mechanisms; attempts must be made to connect genetic and non-genetic susceptibilities with the optimization of our health, driven by the intake of grains.


Rhodotorula toruloides strain, previously selected to produce lipids from sugar beet hydrolysates by completely using the major C (carbon) sources present. An evolved strain, multi-tolerant not only to methanol but to four major inhibitors present in LCH  was isolated and the mechanisms underlying such multi-tolerance were examined, at the cellular envelope level. Results indicate that the evolved multi-tolerant strain has a cell wall that is less susceptible to zymolyase and a decreased permeability, based on the propidium iodide fluorescent probe, in the absence or presence of those inhibitors. The improved performance of this multi-tolerant strain for lipid production from a synthetic lignocellulosic hydrolysate medium, supplemented with those inhibitors, was confirmed.The presence of toxic compounds in lignocellulosic hydrolysates (LCH) is among the main barriers affecting the efficiency of lignocellulose-based fermentation processes, in particular, to produce biofuels, hindering the production of intracellular lipids by oleaginous yeasts. These microbial oils are promising sustainable alternatives to vegetable oils for biodiesel production. In this study, we explored adaptive laboratory evolution (ALE), under methanol- and high glycerol concentration-induced selective pressures, to improve the robustness of a  The presence of toxic compounds in lignocellulosic hydrolysates (LCH) is among the main barriers affecting the efficiency of lignocellulose-based fermentation processes, in particular, to produce biofuels, hindering the production of intracellular lipids by oleaginous yeasts ,2,3,4. MSaccharomyces cerevisiae is an essential experimental model yeast widely employed in biotechnology, natural strains lack many desired traits to be useful for a circular bioeconomy. In particular, they lack the capacity to use a wide range of carbon (C) sources, special metabolic potential, and high multi-stress tolerance. Therefore, the interest in non-Saccharomyces yeasts, like it is the case of basidiomycetes species of the Rhodotorula genus, is gaining momentum  to ensure the stability of the phenotype. The best-performing strain under this stress (IST 536 MM15) was selected and examined in this work, for increased tolerance to different toxic compounds present in the hydrolysates: acetic and formic acids, HMF, and furfural.In the framework of another project aiming to obtain a The evolved strain obtained by ALE, IST 536 MM15, was confirmed in MM medium as being more tolerant to inhibitory concentrations of methanol, compared with the original strain .The tolerance of this methanol-tolerant mutant towards growth inhibitors present in lignocellulosic biomass hydrolysates was tested. This evolved strain was found to be also more tolerant to acetic acid, formic acid, HMF and furfural by comparing the growth curves in MM medium at a pH of 4.5 . This loDepending on the concentration and the inhibitor tested, this may be reflected in a decreased duration of growth latency, a higher maximum specific growth rate and a higher biomass concentration at the stationary phase for the evolved strain, compared with the original strain, under the same level of stress. Regardless of the strain, furfural was the compound with a stronger inhibitory effect for the same molar concentration tested, followed by HMF .To obtain insights into the mechanisms underlying the higher multi-tolerance exhibited by IST536 MM15, compared with the original strain, moderate equivalent concentrations of the four inhibitors were chosen to compare the performance of the evolved strain versus the original one . Growth Arthrobacter luteus, and described as having a powerful effect in the lysing of viable yeast cells [Rhodotorula species [Considering the multi-tolerance of the evolved strain, it was hypothesized that the physicochemical properties of the cell wall could underlie such robustness, as highlighted before ,33. To tst cells . Zymolya species , which w600nm following the addition of zymolyase  in the absence of presence of inhibitors. This experimental evidence indicates that there are differences in the physicochemical properties of the respective cell walls. In general, cells exposed, for a short period of time (1 h), to the different toxicants tested also exhibited altered profiles of zymolyase susceptibility, compared with the non-stressed cells. However, the evolved strain exhibited similar results, compared with the original strain, when cultivated in the absence of inhibitors, with the evolved strain evidencing a significantly less susceptible cell wall, independently of the inhibitor present in the cultivation medium  fluorescent probe. These cells were cultured in the same medium and for the same short period of time used to assess cell wall susceptibility to zymolyase. The original strain cells were used as PI-negative control, and these cells permeabilized by contact with absolute ethanol for 15 min were used as a PI-positive control. The permeability of the original and the evolved strains were compared in the absence or presence of the inhibitors under study .p < 0.05; t-student test). Moreover, the relation of the permeabilities of both strains cultivated with or without the inhibitors for 1 h is, in general, similar to the relation shown above for cell susceptibility to zymolyase, with the original strain being significantly more permeable than the evolved strain. Concerning the eventual alteration of plasma membrane permeability as the result of cultivation for one hour with each inhibitor, most of the alterations observed indicate a less permeable membrane and were observed for the evolved strain for most of the inhibitors, with the exception of furfural, suggesting the occurrence of an adaptive response, as reported before for acetic acid [The higher permeability of cells of the original strain compared with the permeability of the evolved strain, in the absence or presence of inhibitors, is statistically significant  strategy as a powerful tool to improve the performance of a non-conventional yeast species of biotechnological relevance, specifically, to enhance e genome ,27,42, le genome ,44. The Saccharomyces cerevisiae being scarce and the available information concerning non-conventional yeast species of biotechnological relevance. Also for this reason, results obtained in this work for the basidiomycete yeast species R. toruloides are an important addition to current knowledge in the field.Results obtained in this study clearly demonstrate the multi-tolerance of the evolved strain to these four chemical stresses  when compared with the original strain. Furfural, followed by HMF, led to higher growth inhibitory effects for the same molar concentration. Given the higher multi-tolerance of the evolved strain, compared with the original strain, the analysis of the characteristics of the cell envelope, due to its major role as the first line of defense against a wide range of environmental stresses , was conSaccharomyces cerevisiae and in the highly tolerant food spoilage yeast species Zygosaccharomyces bailii [Results from the present study also indicate that the evolved strain has a reduced cellular permeability, in particular a reduced non-specific plasma membrane permeability, compared with the original strain, following 1 h of incubation in the absence or presence of inhibitors. Plasma membrane composition and remodeling under stress are also considered essential for stress tolerance in yeast. In the particular case of acetic acid, the adequate incorporation of sphingolipids and ergosterol in the plasma membrane leads to decreased plasma membrane non-specific permeabilization under acetic acid stress, both in s bailii ,53,54,55s bailii . Given ts bailii ,47, it is bailii , contribs bailii ,57.Collectively, our results suggest that the multi-tolerant phenotype of the evolved strain is, at least partially, the result of a more protective cellular envelope against diverse toxic chemicals present in the external medium. These results are consistent with the notion that there is a crosstalk between the plasma membrane and cell wall biophysical properties, suggesting a coordinated response to counteract the deleterious effects of chemical stresses . These rR. toruloides is capable of using a wide range of carbon sources, this evolved strain, more tolerant to the four major inhibitors present in lignocellulosic hydrolysates, represents an advantage in terms of the exploitation of promising lignocellulosic biomass feedstocks in bioprocesses envisaging the transition to a sustainable bio-based economy.This work can be considered a proof of concept concerning the development by ALE of an efficient oleaginous yeast strain multi-tolerant to four major inhibitors present in lignocellulosic hydrolysates. The objective of the development of industrial strains is to maximize bioprocess productivity (product formation in unit time) and yield (product formation per substrate consumption). The exploitation of ALE to improve the biotechnological potential of nonconventional yeasts was confirmed as a promising strategy. given that the evolved strain exhibits a better performance concerning lipid productivity from a toxic synthetic lignocellulosic hydrolysate. Given that


In everyday clinical practice at a department for oral and maxillofacial surgery, a large number of surgical procedures in the head and neck region take place under both outpatient and inpatient conditions. The basis of every surgical intervention is the patient’s consent to the respective procedure .Particular attention is drawn to the general and operation-specific risks . ParticuIn order to minimize the wound infections/surgical site infections, aseptic operating conditions with maximum sterility are required , 6. FurtAbscesses, cellulitis, phlegmone and (depending on the location of the procedure) empyema are among the most common postoperative infections in the respective surgical area , 4, 8. TRisk factors for the development of a postoperative surgical site infection include, in particular, increased age, smoking, multiple comorbidities and/or systemic diseases  as well as congenital and/or acquired immune deficiency , 11.These, as well as a number of other risk factors, should be recorded as part of the anamnesis of all patients undergoing surgical procedures, particularly in the head and neck region.The majority of postoperative wound infections usually occur within the first postoperative days to weeks, as we were able to impressively demonstrate in a recent publication . As a reInfections in the head and neck region can take a wide variety of clinical courses . The greAlthough sepsis does not show a pathognomonic key symptom, the combination of various symptoms is indicative for a systemic effect deriving from the initial local inflammation of the head and neck region. Typical accompanying symptoms include fever, lack of consciousness, impairment of the cardiovascular system  and an increased respiratory rate .These and other symptoms can be easily detected using the clinically established qSOFA or SOFA score and are crucial measuring instruments in clinical sepsis diagnostics . This isIn particular, the kidney values (creatinine), the platelet count, the leukocytes/C-reactive protein as well as the liver values (bilirubin) show strong pathological changes in septic infections , 19. AnoThe treatment of postoperative septic inflammatory processes, particularly in the head and neck region, requires immediate therapy to counteract the local and systemic spread of the infection. The decisive treatment pillars are, on the one hand, targeted anti-infective therapy (broad-spectrum antibiosis) and, on the other hand, surgical infection treatment, for example in the sense of an abscess drainage, usually accompanied by a surgical opening of the former operation area/stitches.The exact extent of the infection should be estimated intraoperatively and the infected region should be drained, in addition to an incision and antiseptic lavage . DependiThere is a particular risk of a prolonged chronic infection with regard to possible biofilm formation when osteosynthesis material is inserted. Therefore, it may be necessary to exchange the foreign material through (re-)osteosynthesis . FurtherIn order to prevent septic wound infections after surgical procedures, as in the recently published case report described, aseptic surgical conditions and, depending on the surgical procedure and the patient-specific risk factors, perioperative antibiotic therapy are essential .The majority of wound infections often manifest themselves immediately postoperatively, so close follow-up should take place in order to initiate targeted and rapid therapy for this complication after surgical (soft tissue) procedures in the head and neck region.


In genetic engineering, the revolutionary CRISPR-Cas system has proven to be a vital tool for precise genome editing. Simultaneously, the emergence and rapid evolution of deep learning methodologies has provided an impetus to the scientific exploration of genomic data. These concurrent advancements mandate regular investigation of the state-of-the-art, particularly given the pace of recent developments. This review focuses on the significant progress achieved during 2019–2023 in the utilization of deep learning for predicting guide RNA (gRNA) activity in the CRISPR-Cas system, a key element determining the effectiveness and specificity of genome editing procedures. In this paper, an analytical overview of contemporary research is provided, with emphasis placed on the amalgamation of artificial intelligence and genetic engineering. The importance of our review is underscored by the necessity to comprehend the rapidly evolving deep learning methodologies and their potential impact on the effectiveness of the CRISPR-Cas system. By analyzing recent literature, this review highlights the achievements and emerging trends in the integration of deep learning with the CRISPR-Cas systems, thus contributing to the future direction of this essential interdisciplinary research area. CRISPR-Cas systems, derived from a bacterial and archaeal adaptive immune mechanism, have rapidly evolved to become an indispensable tool in biotechnological and biomedical research for genome editing purposes . This reThe Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) and the CRISPR-associated proteins (Cas) form the two principal components of this system. The CRISPR locus in the bacterial genome contains short repetitive sequences interspaced by unique spacers derived from past invaders, such as viruses and plasmids . The traCRISPR-Cas9, a Type II CRISPR-Cas system, has received substantial attention owing to its simplicity and adaptability for genome editing . In thisAn equally promising system is the CRISPR-Cas12, a Type V CRISPR-Cas system. Distinct from CRISPR-Cas9, CRISPR-Cas12 uses a single RNA molecule for both CRISPR array processing and target recognition, and cleaves DNA in a staggered pattern . This syDespite the transformative potential of these CRISPR-Cas systems, accurate prediction of guide RNA (gRNA) activity remains a challenging task. This is where the recent advances in artificial intelligence (AI) methods, particularly deep learning, have begun to show their promise . Deep leAs deep learning rapidly evolves and the utilization of CRISPR-Cas systems continues to expand, it is crucial to continually revisit and evaluate the state of research in this area. Consequently, this review aims to investigate recent advances in deep learning models for predicting gRNA activity. Given the emerging importance of these technologies, this review will be instrumental in informing the ongoing development of accurate and efficient methods for gRNA prediction in CRISPR-Cas systems.The expanding growth of the field of deep learning has underscored its potential in numerous applications, inclusive but not exclusive to the domain of genomics and genetic engineering . The rapThis review encapsulates the last 5 years of research, providing an exhaustive survey of deep learning’s current role in CRISPR-Cas systems. Given the swift growth of deep learning methodologies and the expanding CRISPR-Cas technology, this review is both relevant and indispensable. An examination of current literature reveals successes, challenges, emergent trends, and future possibilities in this domain. The review is expected to guide field researchers and practitioners, catalyzing further breakthroughs in the vibrant convergence of artificial intelligence and genetic engineering.The purpose of this review is to present a comprehensive and focused synthesis of the most recent literature related to the use of deep learning methods in the CRISPR-Cas system. To achieve this, a systematic and diligent approach was adopted in the selection of papers.The first stage of the paper selection process involved conducting a comprehensive search of the academic literature. The Web of Science (WOS) database, a widely recognized and extensive repository of scholarly literature, was primarily utilized for the purpose. Our search terms were carefully chosen to include essential keywords such as “deep learning”, “CRISPR-Cas”, and “neural network” to ensure the identification of relevant articles for our review.This review exclusively incorporates articles published in peer-reviewed journals. This choice was determined by the stringent quality assurance processes that these publications undergo. Peer-reviewed articles are assessed by domain experts, thereby ensuring their scientific credibility. Moreover, they are acknowledged as significant platforms for disseminating scientifically rigorous and influential research.While acknowledging the potential value of preprints and conference papers, a deliberate decision was made to solely focus on peer-reviewed journal articles. This selection criterion aims to uphold the dependability and validity of the review, safeguarding that the incorporated studies have undergone a meticulous vetting process. To maintain the freshness and innovation of this review, specific categories of articles, including review articles and perspectives, were purposefully omitted. This strategy underscores our intention to accentuate the integration of primary, research-based studies in alignment with the objectives of our review.This review pertains exclusively to works published between 2019 and 2023, a timeframe judiciously chosen to ensure the study’s contemporaneity, pertinence, and inclusivity of recent innovations and patterns within deep learning applications for CRISPR-Cas systems. Noteworthy is the data collection for 2023, halted in May, to ensure the review captures the freshest insights and progressions in the domain. The quick advancement of CRISPR-Cas and deep learning technologies dramatically reshaped the landscape of research post-2019, making older studies nearly obsolete and consequently irrelevant for the present review. Given the spectacular progress in deep learning technology in recent years, prior research has been sidelined. Thus, focusing on the studies from 2019 to 2023 is judicious, rendering a meaningful and relevant critique of the state-of-the-art.During the data collection process, additional information, such as the number of citations and publication details for each chosen article, was gathered. This data offered valuable insights into the scope, impact, and acceptance of the research within the scientific community. To provide a structured overview of the deep learning methodologies employed in the selected studies, the papers were categorized based on the specific objectives they pursued. This classification contributes to a comprehensive understanding of the deep learning landscape for the CRISPR-Cas system by enhancing comprehension of the various methodologies utilized. An analytical perspective on the dispersion of the chosen publications divulges insightful patterns and trends. The referenced articles manifest a broad distribution across various renowned journals, underscoring the global interest and engagement in deep learning for CRISPR-Cas systems. This dispersion across myriad scholarly avenues corroborates the cross-disciplinary appeal and reach of this research field.A striking observation is the lack of monopolization by any single journal. The most represented journal accounts for a relatively small fraction, approximately 7.4%, of the total publications. This underlines the expansive and diversified publication landscape, where researchers are disseminating their findings across a multitude of platforms, potentially tailoring their target outlets based on their specialized readership and expertise.Other significant contributors to the publication pool include Biomedicals, Scientific Reports, Nature Biotechnology, and Biosensors and Bioelectronics, each with two publications, constituting around 3.7% each of the total publication distribution. Besides, a multitude of other journals with a single publication, encapsulated under Misc., collectively accounts for 51.8% of the publications, emphasizing the extent and depth of research dissemination in this field. These findings signify that the research on deep learning for CRISPR-Cas systems transcends disciplinary boundaries, warranting publication in diverse scientific outlets ranging from bioinformatics and computational biology to genetics and molecular biology.In order to provide a more robust and statistically driven overview of the deep learning in CRISPR-Cas systems domain, a comprehensive evaluation of the selected academic articles is conducted. This analysis focuses on the distribution of citations and the temporal progression of publications, offering insights into the scholarly influence and the momentum of research in this field.Beginning with citation statistics as a proxy for academic impact and community receptiveness, the median count is found to be seven. This value underscores a healthy level of interaction and endorsement of the considered studies. However, the average citation count, estimated at 16.1, exceeds the median, hinting at the existence of certain highly influential works that skew the mean upwards. This discrepancy between median and mean values reveals a distinct, multifaceted citation scenario among the analyzed papers.Transitioning to a temporal inspection of the publications, an intriguing progression materializes. The year 2019 heralds the research in deep learning for CRISPR-Cas systems, with eight pioneering papers. This is followed by a modest ascension in 2020, where the count escalates to ten. Intriguingly, the year 2021 marks a critical juncture, featuring 17 publications, thereby contributing significantly to this emerging domain. For 2023, despite the incomplete data due to ongoing updates in WoS, five papers have already surfaced until April, suggesting a sustained trend of vigorous scholarly activity. A granular examination of the citation frequency and the year of publication for each paper is encapsulated in This analytical exploration of the selected academic articles offers profound understanding of their scholarly impact and the trends shaping the field of deep learning for CRISPR-Cas systems. The analysis of the median and mean citation statistics illuminates the varied levels of scholarly reception, while the year-by-year publication count demonstrates the momentum of research output. Such insights contribute to an enriched understanding of the academic landscape and lay the groundwork for deeper exploration in this research arena.In CRISPR-Cas systems, a profound necessity exists to predict the activities of gRNAs with high precision and reliability. The spectrum of gRNA activities, spanning from on-target to off-target effects, lays the foundation for successful genome editing. However, the complexity of sequence-activity relationships inherent in gRNAs introduces a layer of intricacy, turning this task into a substantial challenge . TherefoDeep learning, a subset of artificial intelligence, involves neural networks with numerous layers (hence “deep”) that can model complex patterns in large datasets . These nThe application of deep learning in predicting gRNA activities initially took form in studies focusing on the CRISPR-Cas9 system . These sSoon after, researchers began leveraging deep learning for the prediction of gRNA activities in the CRISPR-Cas12 system . CRISPR-Recent advancements in the field have witnessed the emergence of more sophisticated deep learning models that offer greater predictive performance and interpretability. For instance, attention mechanisms, initially developed in the field of natural language processing, have been incorporated into deep learning models for gRNA activity prediction to provide insights into the importance of different nucleotides in a gRNA sequence. Such advancements are indicative of the growing maturity of deep learning applications in this field. In the CRISPR-Cas systems, several studies have proposed distinct methodologies using deep learning models to predict gRNA and sgRNA efficiencies and activities.Studies utilizing hybrid models are evident in the field. For instance, An alternative approach, combining multi-dimensional sequence information with prior knowledge, was adopted by Studies have also proposed methods focused on predicting off-target activities, such as R-CRISPR  and DeepModels focusing on improving the predictability of off-target activities in CRISPR-Cas9 include CRISPR-Net  and noveLastly, a few studies delved into controlling gene expression and improving predictive performance. For instance, Recent advancements incorporate a variety of deep learning architectures, ranging from CNNs to RNNs, particularly those utilizing BiLSTM units, hybrid models, as well as attention mechanisms. This multiplicity of approaches signals the vibrancy and dynamism in the field, reaffirming the overarching view that no single method is universally optimal for every situation, thereby underscoring the need for bespoke solutions tailored to individual requirements.A recurring trend is evident in the frequent utilization of CNNs. This adoption could be attributed to their superior capability to process sequence data, which is particularly pertinent in the context of gRNA sequences. CNNs are adept at identifying local patterns within sequences and are robust to variations in their position, which makes them particularly suitable for gRNA activity prediction.The incorporation of BiLSTMs in numerous models elucidates another prevalent trend. The allure of BiLSTMs stems from their capacity to process sequences in both directions, capturing long-term dependencies in the sequence data. This characteristic makes them especially beneficial for tasks involving sequential data with complex dependencies, such as predicting the activities of gRNAs.Interestingly, there seems to be an increasing interest in combining multiple learning models to form hybrid systems, frequently intertwining CNNs with other machine learning techniques such as SVMs or XGBoost. These hybrid models leverage the strengths of different methods, improving the robustness and performance of the predictive models.Attention mechanisms, a recent innovation, also make their presence felt in the field. Derived from the domain of natural language processing, these mechanisms provide a mechanism to focus on crucial parts of the input sequence, enhancing the interpretability of the deep learning models.Taken together, these trends reflect the swift evolution of deep learning methodologies in the domain of gRNA activity prediction. They indicate the field’s increasing sophistication and maturity while concurrently highlighting the plethora of opportunities that remain unexplored. These advancements, coupled with the constant evolution of deep learning architectures and methodologies, promise a future rich with possibilities for enhanced precision and efficiency in gRNA activity prediction.The epoch of genomic engineering has been decidedly invigorated by the advent of the CRISPR-Cas9 system, an innovative tool that has dramatically transformed the landscape of genetic modification . DespiteThe allure of deep learning lies in its capacity to discern complex, non-linear relationships within data, surpassing the capabilities of traditional linear models . Deep leThe landscape of deep learning in predicting CRISPR-Cas9 editing outcomes is rapidly evolving, with growing interest in the incorporation of attention mechanisms to enhance interpretability. Originating from natural language processing, attention mechanisms direct the model to focus on pivotal parts of the input sequence, offering insights into the influence of different nucleotides in gRNA sequences . This inIn the prediction of CRISPR-Cas9 editing outcomes, multiple studies have been conducted, each offering unique methodologies and insights. For instance, the work presented by The application of CNNs in this domain has also been substantial. Machine learning approaches have also been utilized to identify specific biological changes in cells. For instance, Xenopus and zebrafish embryos. The model successfully predicted repair outcomes leading to a considerable increase in phenotype penetrance in the F0 generation.Exploring predictive modeling further, A substantial contribution to the field of tumorigenesis research was made by Finally, the study by From the comprehensive overview of recent studies as depicted in Secondly, it is notable that studies are progressively endeavoring to augment the practicality of their models by extending their predictive capabilities to diverse scenarios. These encompass a wide range of editing events, including but not limited to the knockout of specific enzymes, off-target cleavage site prediction, and base editing outcome anticipation. This trend elucidates the expanding ambit of applicability for deep learning techniques within this domain, potentially enriching their usability and impact in real-world settings.Xenopus and zebrafish embryos or even in the specialized contexts of human blood cells. This broadening scope of research lends credence to the universality and adaptability of deep learning methods in CRISPR-Cas9 editing outcome prediction.Moreover, the ambit of organisms and cell types under investigation in these studies is also steadily widening. While initial investigations predominantly focused on more commonly used organisms and cell cultures, more recent studies have shown promising results in more complex organisms like Finally, there is an emergent focus on the interpretability and explicability of models, evident in the incorporation of attention mechanisms and comprehensive evaluation of Cas9 variants. These aspects, although often overlooked in a frenzy to achieve higher accuracy, are paramount to the practical adoption of these models, as they provide insights into the model’s decision-making process and thus increase trust in their predictions.The emerging field of applying deep learning methods to predict CRISPR-Cas9 editing outcomes is witnessing rapid evolution. The current trends underscore a concerted move towards more sophisticated, practical, and interpretable deep learning solutions, rendering this an exciting and promising research Frontier.Accompanying the escalating interest in harnessing the potential of CRISPR-Cas systems for genome editing has emerged a critical need for the design of high-activity guide RNAs (gRNAs). A high-activity gRNA, often fundamental to the successful manipulation of genetic sequences, is characterized by its potent ability to direct the Cas proteins to the intended genomic loci with high efficiency and specificity, thereby maximizing on-target activity and minimizing off-target effects . HoweverIn the intricate domain of deep learning, algorithms built upon layered architectures of artificial neurons, or nodes, are furnished with the capacity to learn intricate, nonlinear relationships from copious data . In the One pertinent example is DeepGuide , a neuraIn a similar manner, Investigating guide-target mismatches and their impact on dCas9-sgRNA binding activity, Further extending the analysis to PAM-sequence compatibilities and the on-target and off-target activities of SpCas9 and its variants, The emergence of deep learning has irrevocably revolutionized the landscape of CRISPR-Cas9 system-based research. The drive to ascertain high-activity gRNAs to increase the efficacy of genome editing tasks has seen the advent of increasingly sophisticated computational tools harnessing deep learning. These tools exhibit an ability to fathom the intricate sequence-activity relationships of gRNAs that have hitherto been elusive.A confluence of varied deep-learning architectures has been seen in recent studies. From CNNs to RNNs, these methodologies imbibe essential biological features in their models to aid in the accurate prediction of gRNA activities. This incorporation of biological knowledge into the neural network models has undoubtedly improved their performance, outstripping other existing models and design tools.An innovative trend is the modeling of guide-target mismatches to enhance the accuracy of sgRNA design . LeveragThe deep learning models employed in these studies not only serve to predict but also provide a comprehensive comparison of on-target and off-target activities of SpCas9 and its variants. This permits an evaluation of PAM-sequence compatibilities at endogenous sites in human cells, underscoring the potential for precision in genome editing . As evidIn the era where biological research is progressively influenced by computational methods, the implementation of deep learning-based automated systems has witnessed a progressing interest, especially in the domain of CRISPR-Cas systems. The inception of such systems emanates from the necessity to address the inefficiencies associated with manual handling of the intricacies involved in gRNA design and implementation. Automated systems, empowered by deep learning methodologies, aim to streamline these processes, bringing about a paradigm shift in the execution of CRISPR-Cas system-based experiments.Deep learning algorithms, with their unparalleled proficiency in understanding and modelling complex patterns, form the bedrock of these automated systems . Imbued As a manifestation of this approach, recent studies have unveiled automated systems that offer end-to-end solutions, from the design of high-activity gRNAs to their implementation in genome editing tasks . These sThe incorporation of deep learning in the automation of various procedures within the CRISPR-Cas systems presents another area of research worth highlighting. The research by An interesting approach was presented by The automated systems within CRISPR-Cas research also extends to the development of platforms for high-throughput and precise cell manipulation applications. One such example is the work of Lastly, Leveraging the capabilities of deep learning algorithms not only accelerates the genome editing process but also amplifies its precision and efficiency. From automated injections to single-cell electroporation systems, a clear trend of integrating deep learning with established biological procedures is discernible. The capabilities of deep learning to automate these procedures is a testament to its versatility and power, extending beyond high-activity gRNA design into a broader context of automating procedures within the CRISPR-Cas system.Deep learning’s prowess in image recognition and processing has been pivotal in enabling automated system implementation. The precise localization of target sites within organisms or cells, identification of various cellular structures, and efficient image-based screening have been made possible and more efficient through the use of deep learning algorithms. In particular, CNNs, known for their excellent performance in image-related tasks, have become a mainstay in most works revolving around image analysis for CRISPR-Cas systems.Given these innovative implementations and the upward trajectory in applying deep learning within the CRISPR-Cas system, it can be postulated that the Frontier of genome editing is rapidly expanding. These advancements contribute to the broader scientific understanding of deep learning’s capabilities in bioinformatics, subsequently opening doors for further exploration and innovative applications within genome editing. This trend of increasing reliance on deep learning underscores its potential to drive forward the future of genome editing and our understanding of biological systems at large.As the confluence of deep learning and CRISPR-Cas systems continue to shape a new Frontier in the field of genome editing, novel research avenues are persistently emerging. Amidst the traditional applications in high-activity gRNA design and automation of CRISPR-Cas systems, there is a growing application of deep learning algorithms in diverse domains linked to the CRISPR-Cas system, extending the dimensions of its influence.One remarkable illustration of these emerging topics is the utilization of CRISPR-Cas systems in tandem with deep learning for nucleic acid detection related to specific diseases . This noDiverse novel applications of deep learning in CRISPR-Cas systems present intriguing research trajectories. For instance, In the intersection of deep learning, CRISPR-Cas systems, and protein analysis, Advancements have also been made in the detection and understanding of anti-CRISPR proteins (Acrs), as evidenced by Building upon this, Additionally, deep learning has been employed for comprehensive evaluations of the editing efficiency and specificity of several Cas9 variants, as seen in Lastly, The studies detailed in The discernible trends woven into these recent studies underscore an evident shift towards the convergence of biology and computational science. The expanding reliance on deep learning as a tool for disentangling the complexities of CRISPR-Cas systems signifies a transformative phase in scientific exploration. By leveraging the strengths of these computational techniques, researchers are increasingly able to navigate the intricacies of genomic manipulation with unprecedented proficiency. Moreover, the integration of deep learning in these investigations has not only enhanced the accuracy and efficiency of the resultant solutions but also augmented the scope of research, broadening the horizons of discovery. The maturation of this interdisciplinary approach portends a promising future, one where the symbiosis of deep learning and CRISPR-Cas systems may chart a path to novel scientific breakthroughs.The synthesis and explication of the knowledge generated from the application of deep learning to the ever-evolving field of CRISPR-Cas systems is a task of cardinal importance. Our collective quest for understanding and leveraging the vast potential of these two groundbreaking domains is a testament to the ceaseless ingenuity of the scientific community. This review, which encompasses a substantial and recent corpus of literature, has undertaken the vital role of not only summarizing but also contextualizing these numerous works of investigation within a broader framework.Given the rapid evolution of deep learning methodologies and the ceaseless expansion of CRISPR-Cas technology, it is indispensable to continuously scrutinize, distill, and disseminate the amassed knowledge. The deep learning domain has been characterized by an extraordinarily swift pace of development, creating an abundance of innovative methodologies, which has found a fertile ground of application in the expanding arena of CRISPR-Cas systems. This cross-disciplinary integration, marked by remarkable synergy, is continually yielding novel insights that are poised to redefine the boundaries of genetic engineering.The exploration of the confluence of these two fields, captured in this review, illuminates the transformative potential lying at this intersection. By offering an exhaustive account of the strides made in leveraging deep learning for predicting gRNA activity, the review extends a coherent narrative of progress, successes, and persisting challenges. The importance of this timely discourse stems from its ability to not only guide ongoing research but also to shape the future trajectory of this amalgamated field.In retrospect, the review underscores the necessity of adaptive learning that is sensitive to the swiftly changing landscape of deep learning and CRISPR-Cas systems. Consequently, the urgency to regularly update the state of knowledge in the field cannot be overstated. This endeavor not only facilitates the sharing of insights across the scientific community but also creates a collaborative environment that is conducive to innovation. In doing so, the review plays a pivotal role in fostering a scientific ecosystem that can respond effectively to emerging challenges and capitalize on new opportunities.However, in the recent studies, it has been observed that almost all studies utilize independent datasets, with each paper often employing a dataset specific to the authors, a common characteristic of CRISPR-Cas studies. Since it is widely categorized the studies in this review, each category often targets distinct predictions. As illustrated in The lack of unified reference datasets and common metrics in this field presents a significant challenge in comparing the performance of existing methods numerically. This limitation, inherent to the current landscape of CRISPR-Cas studies, has been considered throughout this review and is worth noting for future studies aiming to advance the field.In conclusion, the comprehensive overview presented in this review provides a vantage point to appreciate the remarkable progress made in integrating deep learning with CRISPR-Cas systems, particularly in predicting gRNA activity. By illuminating the current state of affairs and shedding light on areas of potential growth, the review serves as a critical beacon for researchers venturing into this dynamic interface of artificial intelligence and genetic engineering. Furthermore, by facilitating a rigorous and systematic understanding of the recent advancements in the field, this review contributes significantly to the ongoing endeavor of navigating the uncharted territories in the application of deep learning to CRISPR-Cas systems. The continued pursuit of such interdisciplinary integration promises to unlock transformative potential that can redefine our understanding and manipulation of biological systems.


Existing studies have investigated the relationship between the levels of serum inhibin B (INHB), anti-müllerian hormone (AMH) and precocious puberty in girls, but the results are inconsistent.The aim of this meta-analysis was to assess whether the INHB and AMH levels changed in girls with precocious puberty relative to healthy controls.PubMed, Embase, Cochrane Library and Web of Science were searched through June 2022. We included observational clinical studies reporting the serum levels INHB and AMH in girls with precocious puberty. Conference articles and observational study abstracts were included if they contained enough information regarding study design and outcome data. Case series and reports were excluded. An overall standard mean difference (SMD) between precocious puberty and healthy controls was estimated using a DerSimonian-Laird random-effects model.A total of 11 studies featuring 552 girls with precocious puberty and 405 healthy girls were selected for analysis. The meta-analysis showed that the INHB level of precocious puberty [including central precocious puberty (CPP) and premature the larche (PT)] were significantly increased. While there was no significant association between precocious puberty  and the level of serum AMH.Scientific evidence suggested that the INHB level, but not the AMH level, altered in girls with precocious puberty compared with healthy controls. Through our results we think that INHB level might be a marker for the auxiliary diagnosis of precocious puberty . Therefore, it is important to evaluate and thoroughly investigate the clinical indicators  in order to ensure early diagnosis and medical intervention, and the risk of physical, psychological and social disorders in immature girls with precocious puberty is minimized. Precocious puberty is defined as the development of secondary sexual characteristics in girls by the age of 8 and in boys by the age of 9 . The incIn infancy and childhood girls, only a small number of follicles develops from the primordial to the antral stage, then to be atretic , while tAlthough examination of the ovarian maturation process is limited, early ovarian maturation may be a possible outcome in girls with precocious puberty. Even though it is difficult to pinpoint the precise mechanism of ovarian maturation, AMH and INHB are indicators of ovarian function, and may be used to detect girls at risk of precocious puberty. Therefore, serum levels of INHB and AMH may provide important information to diagnose precocious puberty. Knowledge of the potential associations between INHB, AMH and precocious puberty has public health significance for the diagnosis of precocious puberty in girls. As a result, this systematic review and meta-analysis examined the levels of INHB and AMH in girls with precocious puberty and healthy controls.http://www.crd.york.ac.uk/PROSPERO) under registration number: CRD42022345092. The study followed the guidelines of the Meta-analysis of Observational Studies in Epidemiology (MOOSE)  were significantly increased. While there was no significant association between precocious puberty  and the level of serum AMH.Until now, there has been no meta-analysis that has reported the relationship between level of serum INHB, AMH and precocious puberty. This meta-analysis selected 11 studies and covered 957 girls. The subgroup analyses based on geographical location, number of cases, chronological age of cases, the difference between bone age and chronological age of cases, detection reagent were performed. This meta-analysis indicated that the level of serum INHB in girls with precocious puberty (including CPP and PT) was significantly higher than that in healthy controls, however, no significant difference was observed regarding the level of serum AMH between girls with precocious puberty  and healthy controls.The early diagnosis and treatment of precocious puberty are particularly crucial. Changes of hormone levels, acceleration of physical growth in advance, and epiphyseal fusion ahead of time lead to the final adult height in girls with precocious puberty be lower than that in healthy girls. Furthermore, the secondary sexual characteristics of precocious children appear in advance, but the mental level and psychological development do not, that is, premature sexual characteristics and genital development will lead to psychological and social disorders in immature children . The “8tINHB is useful for determining precocious puberty. As puberty begins, serum INHB level rises dramatically , a risinHowever, the AMH level was not associated with precocious puberty in girls. The level of serum AMH in females is lower than in males during postnatal life, but it starts to rise from 6-8 years old, peaks in late adolescence, and then starts to decline along their reproductive life . In actuSubgroup analysis was conducted to further explore the source of heterogeneity and to assess the correlation between a high level of AMH and precocious puberty in a more accurate manner. Among the cases in this meta-analysis of the correlation between AMH level and precocious puberty, heterogeneity may be present due to region, number of cases, chronological age of cases, the difference between bone age and chronological age of cases, as well as detection reagent use. (1) It is likely that a person’s diet, lifestyle, and economic level will affect how their ovary matures differently in different regions; (2) This meta-analysis included only case-control studies. Co-founders can have a negative effect on the results of cases-control studies if the number of cases is enormous; (3) Physiological age plays a important role in ovarian reserve function, thus chronological age was considered as one of the sources of heterogeneity; (4) The difference between bone age and chronological age of cases can reflect the severity and course of the disease, and AMH level was measured at different stages in different studies. Thus the difference between bone age and chronological age of cases might be one of the sources of heterogeneity; (5) The Gen II ELISA of AMH developed by Beckman Coulter was more sensitive than the older version . TherefoNonetheless, there are a number of limitations to this meta-analysis should be acknowledged. First of all, obvious heterogeneity existed among the AMH level of original studies due to differences in sample size, background of subjects, and the detection reagent used to detect the level of AMH. Secondly, there was no association between precocious puberty and the level of serum AMH, possibly due to the different usage of detection reagents. Furthermore, INHB kits differ widely between lots, potentially also causing bias to the results. In addition, insufficient number of references was also one of the reasons. Therefore, more references are needed to verify this result. Additionally, it is unclear whether the main confounding factors  affecting the ovarian reserve function of girls in original studies were adjusted for. Finally, the references included in this study were all case-control studies, which would limit causal inference. And it takes many years for a child to reach puberty, therefore clinical evaluation and follow-up are still essential. To confirm the clinical importance of AMH and INHB in precocious puberty, and to ascertain the long-term reproductive function in girls with precocious puberty, more studies  are required. In these circumstances, we therefore recommend a conservative interpretation of our conclusions.Overall, this systematic review and meta-analysis showed that the INHB level, but not the AMH level, was significantly higher in subjects with precocious puberty than healthy controls. These meant that the level of INHB was associated with a number of clinical indicators of precocious puberty and could be a reliable marker for girls with disease at an early stage. While the AMH level somewhat influenced the hypothalamo-pituitary-gonadal axis, but more research was needed to investigate its clinical implications in precocious puberty. Furthermore, we recommend that the clinical indicators like FSH, LH, and INHB should be evaluated and comprehensively investigated to ensure that early diagnosis and medical intervention, and to minimize the risk of physical, psychological and social disorders in immature girls with precocious puberty.


National Science Review (NSR) attended this forum, as several presentations related to materials science and information science would appeal to the broad audience of NSR. In the plenary session, four members of the Chinese Academy of Engineering (CAE), Profs. Jiaping Liu, Jianguo Wang, Weimin Zhuang and Hongyuan Mei, provided an overview of the challenges facing construction and architecture industries in the run-up to carbon neutrality.The Natural Science Foundation of China (NSFC) held the 326th Shuangqing Forum on 22 February 2023. As the first forum of 2023 and one of the first in-person activities post-COVID, a topic of national and global significance was chosen: the challenges, for basic sciences, of reaching carbon neutrality in construction and architecture.  China has made two major commitments in the effort to contain and combat climate change: reaching maximal carbon emissions (carbon peaking) in 2030 and reducing net carbon emissions to zero  in 2060. These two goals have naturally created society-wide discussions on alternative power generation and storage, controlled nuclear fusion, the transition to electric transportation, the scaling potential of photovoltaic and electrolytic technologies, etc. One aspect that is receiving increasing attention is how these upcoming emission constraints affect construction and architecture industries as they are responsible for >50% of global carbon emissions. In response, construction engineers have established new policies such as a large-scale reduction of the use of steel and concrete, an increase in the use of steel and timber Figs  and 2, tInterestingly, one crucial challenge of meeting carbon peaking and carbon neutral goals is that they should not be based on experimental measurements but on numerical estimates. Considering that city-centric constructions and surrounding urban activities account for 75% of all emissions of greenhouse gases, accurate and country-wide estimates of carbon emissions will have to be initiated at the construction stage and scaled up from building to district, city and country. At the building stage, the comprehensive implementation of emission-saving technologies can not only improve the quality of residents’ lives, but also reduce emission of wastes and pollutants. Prof. Jiaping Liu of Xi’an University of Architecture and Technology (XAUAT) recommended we pay attention to the research on the basic science of green building design, starting with design principles and methods. However, the performance indexes of green buildings have not been included in the design code and mandatory provisions even though evaluation standards of green building have been developed and related work has been carried out in China for more than 20 years. This shortfall has greatly hindered the realization of the proposed emission goals. It is, therefore, imperative to implement ‘generalized building thermal design principles and methods’ to provide theoretical support for the systemic standards of the overall ‘greening’ of architectural designs.In breakout sessions at the forum, experts from across China had detailed discussions on specific issues, such as how to compile an accurate database of the thermal parameters of construction materials, and methods of scaling emission estimates using artificial intelligence (AI).An accurate estimate of a building’s energy consumption requires precise modeling of heat transfer based on the materials’ thermal parameters such as thermal capacity and thermal conductivity. Prof. Yingying Wang of XAUAT discussed research trends and challenges related to construction materials. Carbon emissions produced by the normal usage of buildings account for 22% of emissions nationwide. There are currently three major methods of energy reduction: optimizing buildings themselves, updating appliances and incorporating renewable energy sources. Optimizing buildings themselves through thermal management receives the most attention from the engineering-physics research community. This step includes determining indoor and outdoor conditions and establishing design principles using a thermal conduction model. A central building block in this process is accurately determining the thermal parameters of construction materials. The team at XAUAT introduced a proper example of the basic science involved: a method to accurately estimate the thermal conductivity of building materials by modeling their moisture content. This was achieved using fractal theory, taking account of the porous and capillary structures of building materials.Industrial buildings belong to a special category in the field of low emission construction and architecture as these designs must include uncommon elements such as sources of pollution, extreme heat and high-pressure airflow. Prof. Yi Wang at XAUAT provided an in-depth look at the unique challenges facing industrial constructions. Major topics in this field include how to balance safety with more complex designs of low emission, numerical simulations of industrial environments, recycling of exhaust heat, and highly efficient designs of ventilation systems. Specifically, improving ventilation design may result in a significant energy saving as ventilation accounts for over 50% of the energy consumption of industrial buildings. One scientific element here is non-invasive velocimetry, which safely and accurately maps airflow in three dimensions. Existing methods include using a doppler laser, particle tracking and schlieren imaging. Schlieren imaging is perhaps a more innovative method that is based on the dependency of the refractive index of air on its density. The experimental procedure consists of imaging a black and white calibration screen through a field of airflow and quantifying the change in light intensity. Using post-processing algorithms, velocity variations of turbulent airflow can be extracted from the intensity measurements.In terms of reducing emissions for individual buildings, an ideal endpoint is sometimes referred to as PEDF, which stands for ‘photovoltaics, energy storage, direct current and flexibility’. Prof. Xiaohua Liu at Tsinghua University provided a description of the concept and an outlook for future directions. The photovoltaic step requires maximal usage of a building's surfaces for renewable energy acquisition, i.e. solar panels. These panels provide the foundation of the PEDF concept. The energy storage step consists of all possible resources as means of energy storage, including the batteries of the appliances inside the building and electric cars parked in the vicinity. The direct-current step emphasizes the usage of high-performance DC motors and DC/DC adaptors for other devices such as lights and computers. The final flexibility step refers to the transition of a building's role as an energy consumer to a composite and dynamic role of supplier-storage-consumer. Key challenges in PEDF include the large-scale transition from AC appliances to DC appliances and optimal allocation of battery capacities. The goal of PEDF is that it replaces the concept of ‘peak hours of electricity usage’ with a constant and ‘much reduced’ energy draw from the power grid, storing electricity drawn at night and spending it during the day. In addition, during peak hours of sunshine, it may send excess photovoltaic energy back into the power grid. Large-scale applications of PEDF will result in more efficient use of the power grid and reduced fuel consumption at power plants.Producing emission estimates for a given building is a complex engineering problem. The sum can be divided into separate components, such as the manufacture of construction materials, transportation, construction, operation and maintenance. Prof. Qingqin Wang of the Chinese Academy of Building Research provided an overview of the existing methods of estimating emissions. They can be divided into two major categories: top-down methods and bottom-up methods. Top-down methods are initiated by values such as Gross Domestic Product (GDP), energy-related income and energy prices to produce a large-scale and macroscopic estimate. Then, using analytical downscaling methods, emission estimates of smaller units can be obtained. Top-down methods are better at reflecting macroeconomic factors but lack technical details. Bottom-up methods are initiated by calculating the emission value of an individual and representative building during a certain time period. By using upscaling methods, larger values of districts or cities can be obtained. The advantage of bottom-up methods is that they provide feedback on specific energy-saving policies so that policy makers can be better equipped to formulate energy-related regulations and strategies. Currently, the majority of countries adopt bottom-up methods.Using emission estimates at various scales as inputs, an optimization method must be used to output preferred architectural designs. In the past, analytical methods were used to find the correlations between construction parameters and green performance in order to produce optimized strategies. However, with the increasing complexity of building parameters and requirements for the reduction of emissions, analytical solutions have become limited in their ability to support complex systems. Prof. Cheng Sun at the Harbin Institute of Technology introduced computational design methods to optimize green performance in construction and architecture. Similar computational approaches in physics, telecommunications and space exploration have proven to be successful, as advancements in computer hardware have merged with new methods in mathematics, engineering and computer science. Specific algorithms adopted in green architectural design include gradient descent, direct search, dynamic planning and genetic algorithms. In more recent years, AI has entered architectural design and made an impact in the same way it did in various other scientific disciplines. Specifically, researchers from around the world have implemented proxy modeling, parameterized modeling and predictive modeling using neural networks to optimize architectural designs. All these attempts have shown that AI-enabled design workflows can significantly simplify modeling procedures and reduce optimization time.Other topics discussed at this Shuangqing Forum included how to incorporate human experiences into quantitative architectural designs, how to properly include special urban features such as historical areas or high-density districts, and how to accommodate extreme weather conditions in architectural designs. As for concluding remarks, Prof. Jianguo Wang of Southeast University made one observation: since the onset of the industrial revolution, humanity has used chemical synthesis to produce construction materials such as steel, glass and cement. This marked the first cornerstone of construction and architecture, as these human-made materials greatly improved the efficiency of the building process, as well as the functions and quality of the final products. The second cornerstone will be the adoption of modern methods to produce the next generation of green construction materials to achieve the same goals with a much-reduced carbon footprint. The summary of Prof. Weimin Zhuang of Tsinghua University emphasized the importance of reducing emissions over full life cycles. Emission reduction will now become a precondition for construction industries, being initiated at the individual building level and scaling up in complexity to cities and beyond. In terms of achieving the most accurate estimates possible, data science and computational methods will be elevated from their current role as mere guidance to being the primary driving force. The overarching principle now is strategy and optimization before construction, and monitoring and evaluation post-construction.


The clinical use of lung ultrasound (LUS) has made more efficient many diagnostic processes at bedside. The great power of LUS is a superior diagnostic sensitivity in many applications, when compared to chest radiography (CXR). The implementation of LUS in emergency is contributing to reveal a growing number of radio-occult pulmonary conditions. In some diseases, the superior sensitivity of LUS is a great advantage, like for pneumothorax and pulmonary edema. Diagnosing at bedside pneumothoraxes, pulmonary congestions, and COVID-19 pneumonia that are visible by LUS but undetected by CXR may be decisive for appropriate management, and even for saving lives. However, in other conditions, like bacterial pneumonia and small peripheral infarctions due to subsegmental pulmonary embolism, the high sensitivity of LUS does not always lead to advantages. Indeed, we doubt that it is always necessary to treat by antibiotics patients suspected of lower respiratory tract infection, who show radio-occult pulmonary consolidations, and to treat by anticoagulation patients with small subsegmental pulmonary embolism. The possibility that we are overtreating radio-occult conditions should be investigated with dedicated clinical trials. Recent advances in point-of-care lung ultrasound made this technique highly impactful in the clinical practice. The advent of the modern use of lung ultrasound represents one of the most impactful and innovative novelties of the last 20 years in the field of the bedside diagnostic process in intensive care and emergency medicine . The powLung ultrasound is mainly based on the interpretation of artifacts. Nevertheless, the diagnostic impact is solid in many applications, especially in emergency and critical conditions. The main power of lung ultrasound, demonstrated in several studies, is the excellent value of sensitivity when compared to the conventional radiologic imaging of the chest . There aFor instance, one of the applications where lung ultrasound demonstrated a great sensitivity is the bedside diagnostic process for pneumothorax. Lung ultrasound, based on the combination of three basic signs, namely lung sliding, lung pulse, and B-lines or consolidations, allows for ruling out quickly and reliably the condition of pneumothorax . In thisAnother application where the high sensitivity of lung ultrasound gives great advantages is the diagnosis of interstitial syndromes. For instance, in the diagnostic approach to decompensated heart failure, detection of B-lines by bedside lung ultrasound is a powerful tool to confirm the diagnosis in doubtful cases, with a far higher sensitivity than chest radiography . The appHowever, there are other applications where the advantages of bedside lung ultrasound, even considering the superiority to chest radiography, may be questioned. There are two main examples of this controversy.The first example is the use of bedside lung ultrasound in the diagnosis of bacterial pneumonia in children and adults. In this application, lung ultrasound again demonstrated a specificity close to chest radiography, but a far better sensitivity when performed at bedside in critically ill patients and in emergencies . Like foThe second example is the use of bedside lung ultrasound in the diagnostic workup of pulmonary embolism. Bedside multiorgan ultrasound of lung, heart, and deep veins can be used to improve the diagnostic workup of suspected pulmonary embolism by increasing the efficiency of the pre-test probability scoring . MoreoveIn recent years much progress has been made that affirmed the diagnostic role of bedside point-of-care lung ultrasound. In many applications, lung ultrasound demonstrated higher sensitivity than chest radiography, revealing many radio-occult conditions that may otherwise remain undiagnosed. In some of these applications, the higher sensitivity of ultrasound imaging represents an unquestionable advantage, like for pneumothorax and pulmonary edema. In some others, the diagnosis of radio-occult pulmonary conditions poses questions about the appropriateness of common conventional treatments. In situation where a therapeutic regimen is burdened by the possibility of side effects, like the use of antibiotics for radio-occult pneumonia or anticoagulants for peripheral subsegmental embolisms, this unanswered question demands urgent response by dedicated clinical research.


Adaptive networks provide a paradigm to investigate natural phenomena in fields ranging from physics, chemistry, biology and neuroscience to physiology, medicine and socio-economic systems . A fundaThe first two articles in the Research Topic consider general synchronization phenomena, while the other four focus on applications in microbial networks, in neuronal systems, and in human physiological networks with the immune system as a reference point.Saha and Dana investigate the dynamics in a system of three identical oscillators coupled in a ring with repulsive interactions. The individual dynamics of each oscillator is described by superconducting Josephson junctions where each node’s state is expressed by phase and voltage variables. Following recent research on adaptive networks  resources. In this work, the resource pool is considered to be influenced by the average activity of the population, whereas the feedback from the resources to the population is comprised of components acting homogeneously or inhomogeneously on individual units of the population. Moreover, the resource pool dynamics is assumed to be slow and has an oscillatory degree of freedom. The authors show how this complex interplay between the population and the resources gives rise to collective activity bursting which is characterized by a recurrent switching between episodes of quiescence and episodes of activity bursts in the population of active rotators. To obtain an analytical understanding for collective activity bursting, they use the Ott-Antonsen reduction for the collective dynamics of the population and singular perturbation theory for the slow flow of the resources. Further, it is shown that the collective activity bursting may stably coexist with a steady state. As one of these regimes could be desirable or undesirable from the applied point of view, Franović et al. discuss two simple approaches that can successfully induce switching between the two regimes.Lücken et al. investigate how complex cross-feeding networks can serve as a basis for exploring natural evolutionary and radiation processes in microbial ecosystems. A characteristic attribute of many ecological networks in the microbial domain is the diversity of substrates, which is an important prerequisite for the immense biodiversity in microbial ecosystems. Using an in-depth numerical analysis, the authors are able to show how microbial species can evolve from a simple initial community, taking into account some elementary evolutionary mechanisms of resource-dependent speciation and extinction. Even in stable environments, the system is subject to persistent turnover, suggesting ongoing co-evolution. In this context, the diversity of the system is self-sustaining because new microbes produce new substrates, which in turn allow new microbes to colonize. A central concern of the authors is to uncover the influence of various parameters, such as mineralization ratio, and metabolic versatility and variability, on the emergent structural properties and substrate formation. This transformation and accumulation of substrates is also found in natural systems such as soils and oceans, which may be important for further biotechnological applications.Khaledi-Nasab et al. investigate the long-lasting effects of coordinated reset stimulation in networks with leaky integrate-and-fire neurons with spike-timing-dependent plasticity. Coordinated reset stimulation is a theory-based stimulation technique designed to counteract neuronal synchrony through desynchronization. Synchronized neuronal activity is commonly associated with various neurological disorders, including essential tremor and Parkinson’s disease. Coordinated reset is a procedure in which phase-shifted stimuli are delivered to multiple neuronal subpopulations for the treatment of refractory Parkinson’s disease. Computational studies of coordinated reset stimulation of plastic neuronal networks demonstrate long-lasting desynchronization effects achieved by down-regulating abnormal synaptic connectivity. In this way, networks are moved into attractors with more stable desynchronized states, so that stimulation-induced desynchronization persists even after stimulation is terminated. A simple model with 103 excitatory leaky integrate-and-fire neurons with spike-timing-dependent plasticity is used for the study. The low computational cost allows the authors to explore the parameter space in detail. Together with analytical calculations, the study shows long-lasting desynchronization effects of coordinated reset stimulation with and without randomization of stimulus amplitudes alone, with randomization of stimulus times alone, and with a combination of both. By varying the coordinated reset stimulation frequency relative to the frequency of the abnormal target rhythm and the number of neuronal subpopulations stimulated separately, the study reveals parameter regions and associated mechanisms in which the two qualitatively different randomization mechanisms enhance the robustness of the long-lasting desynchronization effects of coordinated reset. The results have potential relevance for long-lasting therapeutic effects of randomized brain stimulation.Anesiadis and Provata investigate synchronization phenomena in a multiplex network consisting of two layers. Each layer is represented by a ring of non-locally coupled identical leaky integrate-and-fire neurons. On the other hand, there are one-to-one interactions between the layers. This model represents the two hemispheres of the brain, where within each hemisphere the different brain regions interact only with neighboring regions, while between hemispheres each region is primarily connected to the functionally homologous region. The authors consider both excitatory and inhibitory connections. Different parameter regimes are examined in the numerical study, using the Kuramoto order parameter as an index of synchrony within each ring and the correlation function as an indicator of synchrony between rings. The study shows that the dynamics of the multiplex network can range from coexistence of active and subthreshold domains, solitary states, chimera states, to complete coherence or incoherence. In particular, when there exists weak coupling between the two layers (weak multiplexing), different synchronization patterns are supported in the system of the two rings. These are stable and are found when the values of the intra-ring coupling are close to the critical points separating qualitatively different synchronization regimes, e.g., between the regime of traveling fronts and that of chimera states.Sawicki et al. provide a dynamical systems perspective to the modelling of pathological states induced by tumors or infection. A unified disease model is established using the innate immune system as the reference point. The authors propose a two-layer network model for carcinogenesis and sepsis based upon the interaction of parenchymal cells and immune cells via cytokines, and the co-evolutionary (adaptive) dynamics of parenchymal, immune cells, and cytokines. The interaction of parenchymal cells (metabolism) and the nonspecific immune cells (reaction of innate immune system) are represented by nodes of a duplex layer. The cytokine interaction is modeled by adaptive coupling weights between the nodes. Using this functional model, the authors explain carcinogenesis, tumor progression, and sepsis by destabilization of the healthy homeostatic state (frequency synchronized), and emergence of a pathological state (desynchronized or multifrequency cluster). Thereby, carcinogenesis, organ dysfunction in sepsis, and recurrence risk can be described in a correct functional context. As a guiding example for future work, the authors analyze and interpret the emerging dynamical phenomena due to the presence of pathological cells (tumor) or a dysregulated immune system (sepsis) in dependence on a physiological sum parameter called the age parameter. In summary, the study of Sawicki et al. opens a perspective for further dynamic disease modeling in the context of network physiology.