Modern temperate alley-cropping systems combine rows of trees with rows of crops (agroforestry), which allows for diverse interspecific interactions such as the complementary and competitive use of resources. The complementary use of resources between trees and crops is considered the main advantage of these multifunctional land use systems over cropland monocultures. Moreover, several studies demonstrated that agroforestry systems are environmentally more sustainable than cropland monocultures. Over two decades of research on soil microorganisms in temperate alley-cropping systems are characterized by a variety of different methodological approaches and study designs to investigate the impact of agroforestry on the soil microbiome. Here, we review the available literature on the abundance, diversity, and functionality of soil microorganisms in temperate alley-cropping systems. Further, we identify current knowledge gaps as well as important experimental factors to consider in future studies. Overall, we found that temperate alley-cropping systems increase soil microbial abundance, diversity, and functions as compared to cropland monocultures, which is expected to contribute to enhanced biological soil fertility in these systems. Agroforestry systems are agricultural systems that combine trees or other woody plants with crops or livestock. Several different systems fall under the umbrella of agroforestry. Temperate agroforestry systems include, among others, alley-cropping systems, shelterbelts, orchard meadows, and forest pastures. In the temperate zone, alley-cropping systems are gaining popularity as they can maintain or even increase production while being environmentally more sustainable than cropland monocultures ,2. ModerThe most prominent example of complementary resource use is the altered soil–nutrient (re)cycling in agroforestry systems. In 2004, Allen and coworkers  found evApart from all the benefits, crop yields have been shown to decrease close to the trees as a result of resource competition ,20,21,22Plant health and productivity as well as nutrient cycling are strongly driven by soil organisms, especially microorganisms, e.g., ,25. In rIn this work, we review the available literature on the abundance, diversity, and function of soil microorganisms in alley-cropping agroforestry systems of the temperate zone. Based on our literature review, we provide recommendations for future studies of soil microorganisms in these systems.Microorganisms inhabiting the soil of temperate alley-cropping agroforestry systems have been studied using a variety of methods ranging from microscopy techniques to modern high-throughput sequencing approaches. Among the most frequently applied methods are chloroform fumigation extraction (CFE) for the determination of the microbial biomass ,43,44,45−1) to the tree rows (572 mg kg−1) [Soil microbial population sizes can be quantified using a variety of methods. While some techniques allow for the quantification of specific groups within the microbial population, other methods such as CFE estimate the overall microbial biomass by quantifying mainly C and N within microbial cells, termed microbial biomass C (MBC) and N (MBN), respectively. In 2019, it was demonstrated that poplar trees in alley-cropping systems increased MBC and MBN as compared to the neighboring crop rows . The autmg kg−1) . Similarmg kg−1) . These omg kg−1) . Studiesmg kg−1) ,42. Thermg kg−1)  further mg kg−1) .In addition to the overall microbial population size, several studies quantified specific groups such as bacteria. Seiter et al.  were theThe first investigation of soil fungi in temperate agroforestry systems was conducted by Seiter et al. . By measThe increase in the fungal population size through tree rows has not (yet) been detected in young agroforestry systems (less than five years of age) ,36, and inter alia white ash, silver maple, and Norway spruce. Within an alley-cropping system that combined rows of poplar, white ash, and black walnut trees with rows of soybean, Chifflot and coworkers [The by far most frequently investigated group of soil fungi in temperate agroforestry systems are arbuscular mycorrhizal fungi (AMF). Lacombe et al.  found anoworkers  reportedOverall, soil microbial populations benefit from the presence of tree rows in temperate alley-cropping systems. Several studies also showed that the positive effect of tree rows in agroforestry systems on the microbial population size can extend gradually into the crop rows.The investigation into soil microbial diversity has improved tremendously through the use of molecular tools. Several recent papers have assessed the diversity of soil microbial communities in temperate agroforestry systems using tools such as amplicon sequencing, terminal restriction fragment length polymorphism (T-RFLP), and denaturing gradient gel electrophoresis (DGGE). Here, we compile the available literature on the alpha and beta diversity of soil microbial communities.In 2013, Bardhan and colleagues  used DGGBradyrhizobium, Flavobacterium, Mesorhizobium, and Sporocytophaga spp. were promoted by the tree rows while Bacillus, Lysobacter, Nitrosospira, and Nitrospira spp. were promoted by the crop rows and cropland monocultures [Cortinarius, Geopora, and Inocybe, while crop rows and cropland monocultures favored affiliates of the genera Alternaria, Fusarium, Microdochium, and Mortierella.In contrast to alpha diversity, several studies showed that tree rows in temperate agroforestry systems alter the composition (beta diversity) of soil bacteria. For example, Banerjee et al.  reportedcultures . At the While the Shannon diversity of soil fungi in agroforestry systems has been shown to moderately increase with increasing distance from the tree rows into the crop rows ,37, specIn 2020, van Tuinen et al.  used ampOverall, soil microbial diversity in temperate alley-cropping agroforestry systems is greater than in cropland monoculture systems. This is mainly due to increased beta diversity within the agroforestry systems rather than an increase in alpha diversity: tree rows strongly shape soil microbial communities and harbor a microbiome that is compositionally distinct from that of the neighboring crops. In consequence, the introduction of a tree-row associated soil microbiome into arable land through agroforestry increases the overall diversity of the system.Microbial activities in temperate agroforestry systems have frequently been investigated by measuring potential enzyme activities. Within poplar-based alley-cropping systems, the activities of β-glucosidase, β-xylosidase, N-acetyl-β-glucosaminidase, and tyrosine-aminopeptidase were greater in the tree rows than at multiple distances from the trees within the crop rows . LikewisMeasurements of microbial respiration in response to different substrates (substrate-induced respiration) or water  are common tools to investigate microbial functions. Using 31 C sources, Mungai and colleagues  detected2 fixation, nitrification, and denitrification) in agroforestry systems using real-time PCR [2-fixation gene nifH was increased in the tree row as compared to the crop row and adjacent cropland monoculture at one of their three study sites with poplar-based alley-cropping systems. Therefore, the impact of rows of poplar trees on N2-fixing bacteria (such as cyanobacteria) remains yet to be thoroughly investigated [amoA) genes [cf. [napA, narG, nirK, nirS, and nosZ clade I and II) was increased through agroforestry and was greatest in the tree rows [napA, narG, nirK, nirS, and nosZ clade I and II genes by up to 266, 179, 250, 196, 182, and 88%, respectively [2O in particular) from soil, it has to be noted that the quantification of functional genes reflects the genetic potential of a functional group and may not translate into actual soil functions under field conditions [2O emissions in temperate agroforestry systems are rare and inconclusive but show a tendency towards reduced N2O emissions in proximity to the tree rows cf. [2O emissions by 6 to 36% and increase gross N2O uptake by 27 to 42%. Field measurements of other greenhouse gases were more consistent. For example, the few studies that examined CH4 fluxes under field conditions consistently showed that the integration of tree rows through agroforestry increased CH4 uptake [2 emissions due to increased root respiration of the abundant tree root biomass as well as stimulated microbial respiration through increased C input by the trees, e.g., [2 emissions, temperate agroforestry systems sequester larger amounts of C than cropland monocultures through the biomass of the trees as well as increased SOC stocks [Few studies have quantified functional genes involved in soil-N cycling  and CH4 uptake, while the impact on N2O is less consistent across studies but showed a tendency towards lower N2O emissions in the tree rows than the crop rows and cropland monoculture systems. Furthermore, fluxes of CO2, CH4, and N2O have shown to be affected by the distance from the tree rows.Overall, tree rows in temperate alley-cropping systems increase the catabolic potential, substrate-use efficiency, and the functional diversity of soil microorganisms as compared to crop rows and cropland monoculture systems. These effects are mostly limited to the tree rows and do not extend into the neighboring crop rows. Additionally, populations of functional microbial groups involved in N cycling have been shown to respond to the integration of trees in arable land. Population sizes of denitrifying microorganisms increased in tree rows as compared to crop rows and cropland monoculture systems. Conversely, tree rows decreased the population size of certain bacteria involved in nitrification . The integration of trees through agroforestry increases COAfter decades of agroforestry research in the temperate zone, it is widely recognized that the integration of trees in arable land through alley cropping improves soil quality . Here, we list the main soil alterations through agroforestry and discuss how these changes can shape the soil microbiome with a focus on our findings above. We acknowledge though that these changes act simultaneously, thus, as in other complex systems, it is difficult to disentangle their individual impacts on the soil microbial community.In contrast to the majority of cropland monoculture systems, tree rows in temperate agroforestry systems are not tilled. It is evident that distinct tillage regimes differently affect soil microbial communities, e.g., . No-tillTree rows in temperate alley-cropping agroforestry systems are comprised of woody perennial plants instead of annual crops. The length of the rotation cycle of the tree component is tree species dependent. The aboveground biomass of fast-growing trees with high biomass production  can be harvested repeatedly after few years, whereas trees that produce high-quality timber  are harvested only once after several decades. The increased cultivation cycle length of the trees as compared to the crops allows for the establishment of an herbaceous vegetation layer below the trees that enhances plant diversity . In addiAboveground tree litter in temperate agroforestry systems usually remains on site and decreases exponentially with increasing distance from the trees, e.g., . FollowiOur literature review revealed that soil microorganisms in temperate alley-cropping agroforestry systems have been investigated applying a variety of spatiotemporal study designs. Here, key elements that we consider of importance when studying soil microorganisms in these systems are discussed.We encourage researchers to include a reference land-use system without trees to enable a comparison between cropland agroforestry systems and the default agricultural land-use system: arable land. For temperate cropland alley-cropping agroforestry systems, this would require the inclusion of a spatially close arable cropland managed identically to the crop rows of the agroforestry system. Ideally, the initial soil conditions of the reference land-use system and the agroforestry system should be identical. Several previous studies highlighted the importance of such comparisons for the evaluation of agroforestry systems. We recognize the effort associated with including a reference land-use system; however, we argue that this will enable a fair comparison between agroforestry and other land-use systems.We further believe that future studies should account for the spatial heterogeneity introduced by the trees by sampling along transects spanning from the tree rows to a reasonable distance within the crop rows. Such study designs will also enable area-based weighting of parameters as demonstrated by Schmidt et al.  who collcf. [Spatial heterogeneity within the agroforestry systems not just occurs horizontally but also vertically. For example, deep-rooting roots of trees present below the rooting zone of the neighboring crops can take up leached nutrients  and, thcf. ,46. Howe2-fixing bacteria) are either not studied or highly understudied. Therefore, we encourage researchers to go beyond bacteria and fungi and explore other relevant groups of soil microorganisms in temperate alley-cropping agroforestry systems.Almost all studies that distinguished among different groups of soil microorganisms investigated bacteria and fungi, leaving other microorganisms understudied. For example, so far, no studies on the abundance , comDiscrepancies among study results are likely due to site-specific differences such as different tree and/or crop species, soil management, fertilization regimes, and ages of the systems. We agree with Beuschel et al. , who argSince many agroforestry systems in the temperate zone integrate fast-growing trees with rotation times of around five years, the tree component has fast temporal dynamics. Similarly, crop rotations in the temperate zone commonly span around three to seven crops and may additionally include cover crops. Thus, the crop component of agroforestry systems is also dynamic in time. Finally, differences in soil management and fertilization are directly linked to the cultivated type of crop. Altogether, time is an important factor in agroforestry systems that should be considered when studying soil microorganisms. Although repeated sampling is rather common in other disciplines and often inevitable to understand soil processes, only few studies investigated soil microorganisms in temperate agroforestry systems across time. The investigation of multiple study sites with different tree species, ages, and rotation cycles as well as crop rotations (space–for–time substitution) would be a widely accepted solution to circumvent the issue of repeated sampling in agroforestry systems.Our review revealed that tree rows in temperate alley-cropping agroforestry systems increase soil microbial population sizes, and that this beneficial effect can extend gradually into the crop rows. Additionally, fungi may benefit more than bacteria, as several studies indicated an increase in the fungi:bacteria ratio. Agroforestry systems increase soil microbial diversity mainly through the establishment of a tree–row association microbiome that is compositionally distinct from the microbiome of the crop rows  rather than increased alpha diversity. Furthermore, soil microbial populations in the tree rows of agroforestry system have greater catabolic potential and substrate-use efficiency and are functionally more diverse than those in the neighboring crop rows or in cropland monoculture systems. Microbial communities in soil of agroforestry systems are likely shaped by multiple factors  that act simultaneously but to varying degrees across space and time. To disentangle the effects of these factors on soil microbial communities, future studies require study designs adapted to the complexity of these systems. Overall, temperate alley-cropping agroforestry systems increase soil microbial abundance, diversity, and functionality as compared to cropland monocultures, which is expected to contribute to enhanced biological soil fertility in these systems.


Furthermore, the genomic alterations of the primary lung tumor is captured ex vivo. Lung cancer organoids have become an important preclinical model for oncology studies in recent years. It could be used to model the development of lung cancer, investigate the process of tumorigenesis, and also study the signaling pathways. The organoids could also be a platform to perform drug screening and biomarker validation of lung cancer, providing a promising prediction of patient-specific drug response. In this review, we described how lung cancer organoids have opened new avenues for translating basic cancer research into clinical therapy and discussed the latest and future developments in organoid technology, which could be further applied in lung cancer organoids research.Lung cancer is a complex milieu of genomically altered cancer cells, a diverse collection of differentiated cells and nonneoplastic stroma. Lung cancer organoids is a three-dimensional structure grown from patient cancer tissue that could mimic  Large-scale genomic analyses of lung cancer have shown substantial phenotypic and genetic heterogeneity between individuals represented by intertumoral and intratumoural heterogeneity  and PC9(EGFR del19), demonstrated sensitivity to epidermal growth factor receptor tyrosine kinase inhibitors (EGFR TKIs) Osimertinib, rociletinib, erlotinib, and afatinib, whereas HCC827(EGFR del19) is sensitive to gefitinib or erlotinib or the EGFR inhibitory antibody cetuximab. Previous works showed that the proliferation of cell lines with anaplastic lymphoma kinase (ALK) fusion, such as cell line H3122 (harboring ALK-EML4), could be suppressed by ALK-inhibitors like TAE684 58R and P, the canSecondly, lung cancer cell lines are relatively easy to handle when used to construct certain genetic operations, for example, lenti-virus and CRISPR Cas9 systems, compared with using PDX and organoids model. In addition, the rapid growth of cancer cell lines saves time and labor, and could screen out stable clones and cell subtypes. Numerous studies have used CRISPR Cas9 technique to knock out a specific gene in lung cell line to explore the synthetic lethality and driver mutation. For example, a study has used CRIPSR Cas9 to knock out the EGFR-mutation in H1975 cell lines and the wild-type EGFR alleles in A549 cell line. The study showed that the proliferation ability and tumor volumes of xenografts was suppressed only in NCI-H1975 cell line, which provided a treatment strategy to disrupt oncogenic mutation during cancer therapy . Neverthin vivo microenvironment display vasculature that could provide oxygen and nutrients. The direct implantation of human cancerous tissue circumvents in vitro genetic drift and clonal selection. The in vivo microenvironment also allows cell–cell interaction and communication with host stromal cells and immune cells. These features enable PDX model to retain most of the genomic and phenotype of the derived patient specimen. Therefore, PDX is a great preclinical model for investigating drug response and a promising indicator for clinical therapies. It also is a significant laboratory tool to investigate the mechanisms of drug resistance.Patient-derived tumor xenograft models are developed by grafting and propagating patient-derived lung cancer tissues in immunodeficient or humanized animals, like mice. These patient-derived tissues growing in a three-dimensional Previous studies have shown that NSCLC PDX models with EGFR exon19 deletion mutation and L858R mutation was sensitive to EGFR TKIs gefitinib, erlotinib, dacomitinib, and afatinib, which was in accordance with the clinical results . In addiAlthough there are plenty of advantages of PDX models for lung cancer research, there are some limitations too. For instance, large-scale study is more difficult to perform in PDX models when comparing with lung cancer cell lines models. Besides, construction of PDX models might need more time and are more expensive when compared with lung cancer cell lines models and organoids models. Furthermore, the limited passage number due to a probable adaptive genetic evolution to the murine background in PDX models remained a problem for lung cancer research . Last bu“Lung cancer organoids” refers to the 3D structures processed from lung tumor tissues, containing multiple cell types and growing in an organized manner . Lung cain vivo.Normal function of human organ depends on the synergistic interaction of multiple cell types, which are distributed and organized within a 3D structure . Like noN-acetyl-L-cysteine (NALC), B-27 supplement to maintain the stem cell properties of patient-derived lung cancer cells. But in addition, they added small molecular inhibitors or activators into the culture medium, which included A83-01, Y-27632, Noggin, CHIR 99021, and Smoothened agonist (SAG). In their tailored “cocktail,” some growth factors including fibroblast growth factor 4 (FGF 4), FGF 10, and epidermal growth factor (EGF) were also added  NSCLC organoids could still express the marker of their cancer type such as Ki76 and thyroid transcription factor-1, which supported the expansion of NSCLC organoids models use  . For exaIn the past decades, plenty of lung cancer drug studies performed in standard 2D lung cancer cell lines models were proven to be ineffective because of the genetic drift and clone selection in lung cancer cell lines . Organoiin vitro for organoids models, there is still abundant of growth factors that may affect the differentiation and growth of organoids like molecular in culture medium , and in part by Hong Kong Research Grants Council (RGC), General Research Fund (GRF) (no: 14119019).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Microbiota constitutes part of the inner and outer environment of the plant. This Umwelt underlies steady dynamics, due to complex local and global biotic and abiotic changes. Hence, adaptive plant holobiont responses are crucial for continuous metabolic adjustment at the systems level. Plants require oxygen-dependent respiration for energy-dependent adaptive morphology, such as germination, root and shoot growth, and formation of adventitious, clonal, and reproductive organs, fruits, and seeds. Fermentative paths can help in acclimation and, to our view, the role of alternative oxidase (AOX) in coordinating complex metabolic and physiological adjustments is underestimated. Cellular levels of sucrose are an important sensor of environmental stress. We explored the role of exogenous sucrose and its interplay with AOX during early seed germination. We found that sucrose-dependent initiation of fermentation during the first 12 h after imbibition (HAI) was beneficial to germination. However, parallel upregulated AOX expression was essential to control negative effects by prolonged sucrose treatment. Early downregulated AOX activity until 12 HAI improved germination efficiency in the absence of sucrose but suppressed early germination in its presence. The results also suggest that seeds inoculated with arbuscular mycorrhizal fungi (AMF) can buffer sucrose stress during germination to restore normal respiration more efficiently. Following this approach, we propose a simple method to identify organic seeds and low-cost on-farm perspectives for early identifying disease tolerance, predicting plant holobiont behavior, and improving germination. Furthermore, the research strengthens the view that AOX can serve as a powerful functional marker source for seed hologenomes.Plants respond to environmental cues  Understanding the role of microbiota in adaptive plant robustness is important for crop improvement and developing innovative tools that could allow more efficient plant selection . ResearcHowever, there is a lack of knowledge on traits that aid in (a) early prediction of the plant strength, (b) demonstration of its relevance for plant-microbe interactions, and (c) transformation of such knowledge toward user- and environment-friendly applications for sustainable agriculture. We earnestly aim with the perspective to understand these phenomena and to contribute to the knowledge base toward closing these three gaps.per se is recognized as a marker for adaptive plant robustness  are known to interact with redox-sensitive protein cysteine thiol groups relevant for energy metabolism and metabolic channeling linked to cell differentiation and cell cycle regulation . Sugars de novo programming. Ethanol has been shown to reduce ROS levels and led to high induction of alternative oxidase (AOX) and glutathione-S-transferase transcripts (nitrate reductase (NR). Furthermore, the data signaled activation of cell death-regulating system and arrested cell cycle by reducing alpha-tubulin gene transcription in the earliest step of reprogramming. Considering the generality of these observations, we proposed a reference transcriptome profile to identify virus traits that link to harmful reprogramming  and acted positively on germination, independent of auxin-coordinated hormonal crosstalk linked to abscisic acid suppression and gibberellin activation  are dominating metabolic reactions . During S levels . Also, tS levels . Ethylentivation . During tivation . Eckert AOX genes in plant holobionts  during Arabidopsis thaliana seed germination alcohol dehydrogenase (ADH) transcript levels were increased at 12 h after seed stratification (SL) in water followed by a decline, and the increase in ADH transcript levels is in general accompanied by increased AOX1a transcript accumulation ((b) In agreement with (a), germinating carrot seeds displayed a higher level of ADH activity at 12 HAI than 24 HAI. In the presence of 3% sucrose, this level was further enhanced ((c) Two hours short pulses of sucrose before water imbibition enhanced early germination in seeds of two different species, viz., carrot and wheat ((d) On the contrary, SHAM treatment until 6 and 12 HAI suppressed germination in the presence of 3% sucrose. However, it favored early germination in the absence of sucrose (mulation . (b) In enhanced . (c) Twond wheat . Additiond wheat . (d) On  sucrose .(e) Three carrot native bacterial endophytes  were used for carrot seed inoculations with two cultivars  and showed a tendency to improve germination ((f) Sucrose has displayed different impacts on endophyte-mediated effects on germination and is dependent on cultivar and endophytes. However, in no case did endophytes improved germination of sucrose-treated seeds when compared with endophyte-treated controls without sucrose ((g) In a good germinating carrot cultivar (cv. Kuroda), the two selected Rhizophagus species (Rhizophagus irregularis and Rhizophagus proliferus) acted negatively on early germination, while in a delayed germinating carrot cultivar (cv. Early Nantes), both Rhizophagus species acted positively  improved germination of both cultivars compared to M1-treated control seeds in the absence of sucrose ((h) In addition, at lower concentrations of SHAM (5 mM), early germination could be improved to higher levels as compared to the AMF-treated controls (mination . Howevermination . (f) Suc sucrose . (g) In sitively . In both sucrose . (h) In controls , but thicontrols .via enhanced glycolysis, pyruvate production, and increased tricarboxylic acid (TCA) cycle, in a way that the respiratory chain can get overloaded by electrons followed by enhanced ROS/RNS levels and, on the other hand, restricted due to rapidly consumed oxygen and/or yet low numbers of functional mitochondria concerning the presence of oxygen during germination. In response, aerobic alcoholic and lactic fermentation are stimulated  transcripts of 346%. Warburg effects are increasingly recognized also in human systems  while non-AMF-treated seeds respond upon sucrose typically with a delay in germination  could improve early germination to higher levels than observed in AMF-treated controls. This observation is in agreement with the palliating effects observed by Scutellospora calospora on negative SHAM effects on carrot germination by using the same cultivar  Seed selection with the help of short germination tests under SHAM discrimination. This tool provides modalities to identify seeds with higher seed vigor, general adaptive plant robustness, and superior internal seed quality related to the content of secondary metabolites , 4.(B) Discrimination of organic vs. conventionally produced seeds with the help of short duration germination tests in water solutions with 5% commercial sugar .(C) Germination improvement by 2-h pulses of commercial sugar .Furthermore, we encourage developing novel tests for AMF functionality in germinating seeds in the presence of sucrose. This approach targets compatibility between selected plants and AMF strains to support plant holobiont plasticity.AOX gene sequences of symbiotic partners can impact plant–AMF compatibility. Therefore, we want to accomplish wider screening of major AOX polymorphisms in species-specific target cells for evaluating plant performance .We hypothesize that the observed integration of bacterial endophytes into host plants with similar sensitivity against SHAM effects might point to synchronized AOX regulation in plant holobionts. Into this derivation would fit that we observed the same tendency of inhibiting sucrose effects on endophyte-free and superficially sterilized seeds , which wThis present perspective is complementing The authors want to dedicate their work to FREEDOM and ETHICS.The original contributions presented in the study are included in the article/Arabidopsis, and AK carried out the ADH analyses on chickpea. BA-S performed on-farm analyses (RB performed lab analyses on carrot germination, endophyte isolation, and inoculation trials related to analyses . CN was The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Background and Aims: Internet gambling has recently grown in popularity, but relatively little is known about how online and the combination of online and offline (mixed) gambling are associated with gambling disorder (GD) and related problems. The present research examined in a cohort study sample of young Swiss men how their gambling activities and gambling-related problems differed across the spectrum from offline to online gambling.Sample: A general-population based sample from the Cohort Study on Substance Use Risk Factors (C-SURF), consisting of 5,352 young Swiss men (mean age 28.26 years old).Measures: The spectrum from exclusively offline to almost exclusively online (>90% of gambling money spent online) gambling was measured using one question about the proportion of gambling money spent online. Total money gambled and time spent on gambling were also assessed. GD severity (range 0–9) was measured using items reflecting the nine DSM-5 GD criteria. The number of gambling-related problems , other addictive disorders and mental health problems were also inquired about.Methods: We estimated a generalised linear model using a count model  for GD severity and gambling-related problems associated with the amounts and proportions of money gambled online and offline.Results: The number of GD criteria were associated with money gambled online  and offline . This was also found for the number of gambling-related problems . Compared with exclusively-offline gamblers, mixed gamblers (26–90% of money gambled online) showed the highest levels of GD symptoms and gambling-related problems, followed by the almost-exclusively-online gamblers (≥91% money gambled online) and, overall, these associations were still significant after adjustment for overall involvement in gambling (time spent and money gambled). Levels of other addictive disorders and mental health problems were higher among mixed gamblers than among offline-only gamblers, but levels among almost-exclusively-online gamblers were not.Conclusions: Symptoms of gambling disorder and gambling related problems are highest among gamblers engaging in both offline and online gambling. Prevention efforts need to target the combination of offline and online gambling. Gambling is a common leisure activity in Switzerland, with 69.0% of the general adult population being lifetime gamblers and 55.0% having gambled in the last 12 months . After abehavioural addiction currently fully recognised by the DSM-5  and GD symptoms and gambling-related consequences.The second aim was to investigate online vs. offline gambling from a different approach, testing how GD symptoms and gambling-related consequences differed across the spectrum from offline to almost-exclusively-online gamblers. It would also look at the degree to which these associations were due to greater involvement in mixed and almost exclusively online gambling.Finally, because findings regarding the associations between online gambling and mental health are relatively few and heterogeneous across studies , the stuhttps://www.c-surf.ch/en/1.html. Sixteen were excluded because of missing values on main variables, resulting in a sample size of 5,352. Furthermore, 300 participants replied after 14 February 2020. Although their responses may have been affected by the COVID-19 crisis, after careful evaluation, we concluded that the non-response bias introduced by excluding these late-responders , cobeta per CHF 100 gambled for the linear regression models for other addictive disorders and indicators for mental health. For Aim 2, differences in GD symptoms and gambling-related problems across the spectrum from offline to online gambling were tested using negative binomial regressions, with offline-only gamblers being the reference group. IRRs are reported for negative binomial regressions. In a second step, these analyses were adjusted for the time spent and money gambled to account for differences in involvement in gambling. The prevalence for each of the 10 gambling related problems and of reporting any of the 10 problems was calculated separately for each category of the proportion of money spent online. Chi-square tests were performed to test whether these individual problems differed significantly across the spectrum from offline to online gambling. For Aim 3, the analyses made for amounts of money gambled online and offline (as in Aim 1) and for the spectrum from offline to online gambling (as in Aim 2) were repeated for addictive disorders  and mental health indicators (linear regressions). All analyses were adjusted for age and linguistic region (French vs. German) and carried out using SPSS 25 software.Descriptive statistics were calculated for the overall sample and by category of the proportion of money gambled online. For Aim 1, negative binomial regressions were used to test associations between the outcomes' GD criteria and gambling-related problems. In a first step, this was done bivariately, and in a second step, amounts of both online and offline money were entered into the regression model together. The resulting coefficients were multiplied by 100 and then log transformed to get an incidence rate ratio (IRR) per CHF 100 gambled (for better readability) and a Descriptive statistics are presented in Hours per week spent gambling and money gambled per year were lowest in the offline gambling group, peaked in the mixed group and were again a bit lower in the almost exclusively online gambling group . Figure Compared to exclusively-offline gamblers, numbers of GD symptoms were significantly higher among mostly-offline gamblers , mixed gamblers , and almost-exclusively-online gamblers , with the peak being among mixed gamblers see . DiffereRegarding more distal correlates, offline gamblers showed significantly higher levels of alcohol, cannabis, and tobacco use disorder than non-gamblers, but not for illicit drug use, gaming and internet addiction, nor for indicators of mental health . CompareThe present study had three aims. First, to analyse the association between online and offline gambling involvement and GD symptoms and gambling-related problems. Second, to look at groups of gamblers according to their proportion of online and offline gambling on GD and problems, and third, to look at the associations between on- and offline gambling with other addictive behaviours and mental health.Overall, our participants spent about three times as much gambling money offline than online. Two thirds of gamblers  gambled exclusively offline, and this group spent considerably less time gambling and gambled less money than those who also played online. Mixed gamblers (1–90% of gambling money spent online) represented 7.0% of the total sample (30.1% of gamblers), whereas almost-exclusively-online gamblers (≥91% of money gambled online) represented about 1.6% (5.4% of gamblers).As regards our first aim, we found that both the amount of money spent on- and offline were associated with GD and gambling-related problems. These findings were consistent with a recent meta-analysis that found that both internet gambling and offline gambling activities were strong risk factors for problem gambling .As regards our second aim we categorised participants into groups according to the proportion of money they gambled online and compared them  with respect to gambling-related problems. Mixed gamblers spent a lot more time and more money on gambling than exclusively offline gamblers, and they showed higher levels of GD criteria and gambling-related problems. However, almost-exclusively-online gamblers (≥91% of money gambled online) fell in-between offline and mixed gamblers as regards time spent, money gambled, GD criteria and gambling-related problems. Thus, there appeared to be an inverse-U shaped association across the spectrum from offline to online gambling and gambling-related problems, with problems peaking among mixed gamblers. These findings were in line with the review by Gainsbury  and someRegarding specific gambling-related problems, it is noteworthy that almost-exclusively-online gamblers reported interpersonal problems and financial problems for someone close rarely especially compared to mixed gamblers. A possible explanation for this is that online gambling can more easily be kept secret from one's entourage and may be less noticeable; it may therefore create fewer interpersonal conflicts, especially for young men who have fewer social roles and responsibilities than older adults. In line with these findings about interpersonal conflicts, almost-exclusively-online gamblers did not have higher levels of social anxiety disorder than offline gamblers, whereas mixed gamblers did. This is particularly remarkable because one might expect individuals with higher levels of social anxiety disorder to tend to engage in solitary gambling activities online . HoweverAs regards our third aim, compared to offline gamblers, our sample's mixed gamblers  reported higher levels of other addictive disorders , major depression and social anxiety disorder, and they also showed lower life satisfaction. Almost-exclusively-online gamblers, however, only showed a significantly higher level of internet addiction, which is unsurprising given that online gamblers probably spend more time on the internet than offline gamblers.Overall, both online and offline gambling are associated with gambling disorder, gambling-related problems, other addictive disorders and mental health problems. Compared to offline-only gamblers, gamblers engaging in both offline and online gambling appeared to be at a higher risk not only of GD and gambling-related problems but also of other addictive disorders and mental health problems. To date, findings in the literature about associations between gambling and mental health comorbidities have been heterogeneous, with some studies finding an increased risk for mental health comorbidities in online gamblers, while others did not . Thus, oAlthough our sample only included young Swiss men, young men are a group with a high risk of gambling-related problems. Our general population-based sample provided a different perspective from surveys among gamblers only. The case numbers of individuals with serious gambling problems were small, therefore any conclusions applicable to clinical practise should be done so with great care. Our study did not include detailed measurements of the precise gambling activities engaged in online and offline or the amounts of money gambled and time spent on those individual activities. Such information would be valuable to gain a better understanding of which specific gambling activities were most associated with gambling-related problems. Furthermore, the cross-sectional nature of our results precludes any inference as to the direction of causality, i.e., whether online gambling causes problem gambling or whether gamblers with existing problems tend to use online gambling more often as it is readily available. Finally, about 5% of our sample were late responders, replying to our survey after the onset of the COVID-19 crisis in Switzerland in February 2020, which may have affected their gambling behaviour. However, the time frame for the questions asked was “in the last 12 months,” and we provided a sensitivity analysis without these 300 participants and the results were overall similar, however, in some cases, coefficients were no longer significant without these 300 participants. Overall, the inclusion of these late responders did not alter the conclusions drawn from our study. We decided to use the full sample because excluding late responders (who may differ from early responders)  may intrWe used two complementary analytical approaches to investigate the associations between involvement in online gambling and gambling-related problems in a large general-population sample of young Swiss men. In our first approach (Aim 1), online gambling and offline gambling both contributed to gambling disorder symptoms and gambling-related problems, and both were associated with other addictive disorders and mental health problems. Our second approach (Aim 2) showed that the peak involvement in gambling, gambling-related problems and mental health comorbidities (Aim 3) was among mixed gamblers. Thus, it appears that the combination of offline and online gambling is associated with most gambling-related problems. Prevention efforts should address both online and offline gambling, but they should also consider interactions between these two domains of gambling. Apart from their risks, online gambling environments may also provide good opportunities to promote responsible gambling using tools that can be personalised to the individual gambler . It coulThe raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by the Human Research Ethics Committee of the Canton of Vaud. The participants provided their written informed consent to participate in this study.SM contributed to the questionnaire design, conducted the data analysis, and wrote the initial draft of the manuscript. MW, GG, JS, and YK contributed to the questionnaire design, data analysis plan, interpretation of the results, and the writing of the manuscript. GG was responsible for the development of the questionnaire and supervised the data collection, and the writing of the manuscript. All authors approved the final version of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Immune and cancer cells compete for nutrients within the tumor microenvironment, leading to a metabolic battle between these cell populations. In this battle, tumor cells reprogram their metabolism for a high demand of building blocks and energy and to gain advantages over immune cells. To study these mechanisms, we require the quantification of metabolic fluxes, which can be estimated at the genome-scale, with constraint-based or kinetic modeling.The tumor’s physiology emerges from the dynamic interplay of numerous cell types, such as cancer cells, immune cells and stromal cells, within the tumor microenvironment. Immune and cancer cells compete for nutrients within the tumor microenvironment, leading to a metabolic battle between these cell populations. Tumor cells can reprogram their metabolism to meet the high demand of building blocks and ATP for proliferation, and to gain an advantage over the action of immune cells. The study of the metabolic reprogramming mechanisms underlying cancer requires the quantification of metabolic fluxes which can be estimated at the genome-scale with constraint-based or kinetic modeling. Constraint-based models use a set of linear constraints to simulate steady-state metabolic fluxes, whereas kinetic models can simulate both the transient behavior and steady-state values of cellular fluxes and concentrations. The integration of cell- or tissue-specific data enables the construction of context-specific models that reflect cell-type- or tissue-specific metabolic properties. While the available modeling frameworks enable limited modeling of the metabolic crosstalk between tumor and immune cells in the tumor stroma, future developments will likely involve new hybrid kinetic/stoichiometric formulations. Tumors have a pseudo-organ-like dynamism with numerous cell types that interact to create a unique physiology . The celThe immune effectors against cancer are natural killer cells (NK), dendritic cells (DC), macrophages, polymorphonuclear leukocytes (PMN) , mast cells and T cells (lymphocytes B and cytotoxic T lymphocytes). NK cells, DC, PMN, mast cells and macrophages constitute the first-line effectors to cancer cells . NK cellBased on the expression of cell surface markers T cells are classified into CD4+ helper T (TH) and CD8+ cytotoxic T (TC) functional cell lineages. TH cells bind to the class II major histocompatibility complex (MHC) molecules and help the immune system recognize foreign substances to modulate the immune response and to produce cytokines to enhance or suppress inflammation . TC cellThe TC activation requires two signals: a first signal the TCR binds to the MHC class I molecule, and a second signal assisted by stimulating the CD28 molecule on the TC with cytokines released from TH cells. Then, using IL-2 acting as a growth and differentiation factor for the TC, the TC clonally expand, increasing the cells that recognize the antigen .Before encountering an antigen, T cells are called T naïve (TN) and after antigen encounter, T cells are activated. Regulatory T cells (Tregs) emerge from the acquisition of immuno-suppressive characteristics of the TH cells. TC cells can differentiate from TN to stem central memory T cells (TSCM), to central memory T cells (TCM), effector memory T (TEM) or effector T cells (TEFF). All of these cells finally converge in terminally differentiated effector memory cells re-expressing CD45RA (TEMRA) . TN cellMemory T cells can be CD4+ or CD8+ and are TEM or TCM subtype depending on the set of cell surface markers they express. All of them are long-lived and capable of expanding quickly when re-exposed to their corresponding antigen, which provides a controlled immune response against previously encountered foreign bodies.T cells rewire their metabolism to promote these changes. TN cells primarily rely on glucose respiration and oxidative phosphorylation (OXPHOS) to meet their energy needs. Upon recognizing an antigen, activated T cells differentiate to TEFF and switch to aerobic glycolysis, increasing both glucose and glutamine uptake . Once thThe metabolic phenotype of cancer cells reflects the characteristics of any proliferating cell; thus, cancer and immune cells have similar metabolic requirements , and theMetabolic reprogramming and immune escape are two major hallmarks of cancer cells. Metabolic reprogramming is not only indispensable for sustaining tumorigenesis but also for maintaining homeostasis of immune cells. Immune and tumor cells compete for metabolic resources within the TME, as they share metabolic needs. However, compared with immune cells, cancer cells show greater plasticity to reshape their metabolism to circumvent the adverse conditions and increase the ability to capture and exploit the limited metabolites available within the TME. This leads to an interaction between tumor and immune cells that results in a metabolic competition within TME that limits nutrient disposition and conditions the function of immune cells and often results in immunosuppression ,26. IndeCancer triggers immune escape thought several mechanisms including reduced expression of antigens at the surface of tumor cells, reduced expression of MHC molecules by APCs, impaired co-localization of TCR and co-stimulatory receptors, secretion of inhibitory cytokines and activation of inhibitory receptors on T-cell surface, as well as metabolic rewiring . The infEven in the presence of oxygen and fully functioning mitochondria, tumor cells preferentially use aerobic glycolysis instead of oxidative phosphorylation, which supports the production of building blocks, reductive power and ATP for proliferating cells, but it has lower ATP yield per molecule of substrate, a phenomenon termed the Warburg effect . This meGlucose and glutamine are the main substrates of cancer cells; thus, their uptake is increased in cancer. Glutamine metabolism is mainly driven by the C-MYC oncogene, a proto-oncogene often constitutively overexpressed in cancer, which increases the expression of glutamine transporters and their metabolic enzymes ,27  and promotes acidification within the TME. Additionally, acidification can be potentiated by membrane CA-IX upregulated by the HIF pathway. CA IX promotes acidification in the TME since its active site faces the extracellular space and catalyzes CO the TME . The pro the TME .Acidification of the TME provides a growth advantage to tumor cells at the expense of immune cells, as it inhibits proliferation and function of T cells, affects the function of monocytes and NK cells, and acts over the chemotaxis and migration of neutrophils and DCs while promote Treg formation, myeloid-derived suppressor cells (MDSCs) and M2 polarized macrophages infiltration with greater immunosuppressive effects on T cells  Figure .BIN and KIT oncogenes ) and BIGG models database [http://bigg.ucsd.edu/ [Accessed on 1 January 2020]).Context-specific GEMs have a wide range of applications, such as identifying putative drug targets , predictic atlas  (https:/database  (http://S × v) results in a system of ordinary differential equations (ODEs) describing the metabolite concentration changes over time. The equations for the reaction rates are functions of enzyme concentrations, kinetic parameters and metabolite concentrations, so it is possible to describe the stoichiometric relation between substrate and products considering the enzymatic mechanisms and different levels of regulation , making these methods appropriate for multiple omics data types’ integration. Therefore, solving such models enables dynamic analysis of biological systems and allows quantitative predictions of the cells’ states along time for enhanced in silico hypothesis generation. Kinetic models have been typically used to generate efficient strain optimization algorithms ) is a good resource of cancer high-throughput-omics data that can be collected and integrated to holistically perform analysis, using GEMs. Outside of cancer, these bottom-up systems’ biology approaches that drive drug repurposing and drug discovery can be used to identify therapies for other diseases [Future developments will likely involve methodological and translational advancements, such as using richer datasets with additional data sources from different cell types from scRNA-Seq and clinical samples that include the information of cellular regulatory mechanisms or modeling interactions of cancer cells with the surrounding cells from the TME, as well as predicting and circumventing the drug resistance of metabolic cancer drugs by building integrated kinetic and stoichiometric models of cancer metabolism. For example, The Cancer Genome Atlas (TCGA) database (diseases .Expanding the understanding of the dynamic interplay of numerous cell types, such as cancer cells, immune cells and stromal cells within TME is likely key to develop effective cancer therapies. Cancer cells reprogram their metabolism to capture substrates needed to sustain proliferation with increased avidity and produce toxic metabolic byproducts, inhibiting immune cells’ function and promoting immune evasion. Thus therapies selectively targeting the metabolic crosstalk between cancer and immune cells could potentially restore immune function within the tumor, leading to improved therapeutic outcomes. In this regard, the GEMs approaches reviewed in this work are promising tools to analyze the metabolic crosstalk underlying immunosuppression in the TME, stratifying patients, identifying key players promoting immunosuppression in each patient strata, and repositioning drugs to target them.


Developmental Cell, Sen, Harrison et al. propose that the risk of mis-segregation in human mitotic cells is higher than previously thought and identify the existence of an early-anaphase correction mechanism. The study documents kinetochore dynamics in unprecedented detail, providing a detailed look at the events preceding loss of correct chromosomal numericity and genomic stability.Mitotic divisions achieve equal re-partition of chromosomes into daughter cells. In their recent work in  They quantify metaphase sister chromatid behaviour as a predictor for lagging chromosomes in anaphase and collect evidence for the existence of an early anaphase correction mechanism orchestrated by the kinase Aurora B.The fundamental importance of cell division lies in equal distribution of sister chromatids into budding daughter cells, ensuring genomic integrity. Failure to do so results in aneuploidies and the potential rise of micronuclei as distinct entities from daughter nuclei, both ultimately leading to genomic and transcriptomic instability and the promotion of cancer. During mitotic anaphase, chromatids that lag behind are the main culprits for mis-segregation, which are caused by improper attachments of kinetochores such as merotelic attachments (where one or both sister kinetochores are linked to opposing poles). Previously, the rate of lagging chromosomes in human cells was estimated at 5%; however, imaging limitations and data relying on fixed samples provide an incomplete picture. Using lattice light-sheet imaging and computational analysis of 3D kinetochore trajectory tracking, Andrew McAinsh, Nigel Borroughs and colleaguesThe tracking pipeline developed by the authors allows for a quantifiable definition of lagging chromosomes, and they coin the term ‘laziness’ for the distance a kinetochore takes from its segregating cluster above a certain threshold. In doing so, Sen, Harrison et al. find that the number of cells with lazy, as opposed to timely, kinetochores and thus at risk of mis-segregation at anaphase, is at 18%—a value 2–3x higher than previously assumed. Connecting the behaviour of sister chromatids in metaphase to their lagging state in anaphase, they find that a small distance between sister chromatids is a strong predictor of lagging in anaphase; more importantly, kinetochores with reduced oscillatory dynamics are persistently lazy in anaphase. The authors then compare the shape of kinetochores to their behaviour and find stretched kinetochores, often associated with merotelic attachments, are more likely to be persistently lazy in anaphase.To investigate an error correction mechanism of kinetochore-microtubule attachments, the authors then examine the role of the Aurora B kinase in anaphase. Upon the inhibition of Aurora B, they find an increase of lazy (and stretched) kinetochores. Further, kinetochores oriented towards the midzone display a preferential accumulation of Aurora B-dependent phosphorylation, a readout for error-correcting attachment destabilization. Interestingly, experimentally preventing Aurora B’s localization to the anaphase spindle midzone boosts the occurrence of micronuclei, a potentially catastrophic outcome to chromosome mis-segregation.This work provides a comprehensive view of the dynamic behaviour of kinetochores and sister chromatids through mitotic meta- to anaphase, identifying a high occurrence of lagging chromosomes in dividing human cells. Aurora B, besides its well-known pre-anaphase error correction role, also monitors chromosome position in anaphase for accurate daughter cell formation. The authors here assign an early-anaphase correction role to Aurora B via an attachment-destabilising gradient. Thus, the manuscript establishes that nearly one fifth of dividing human cells have lazy chromosomes, and continuous monitoring by Aurora B at time of separation prevents the worst. However, as the authors point out, the impact of spindle forces in anaphase and the recent finding that Aurora B stabilizes kinetochore-microtubule attachments in anaphase will have to be reconciled with these findings in future work, as well as the direct demonstration of micronuclei formation and microtubule occupancy at kinetochores for lazy chromosomes.


Coronary artery disease is a major cause of morbidity and mortality worldwide. Its underlying histopathology is the atherosclerotic plaque, which comprises lipid, fibrous and—when chronic—calcium components. Intravascular ultrasound (IVUS) and intravascular optical coherence tomography (IVOCT) performed during invasive coronary angiography are reference standards for characterizing the atherosclerotic plaque. Fine image spatial resolution attainable with contemporary coronary computed tomographic angiography (CCTA) has enabled noninvasive plaque assessment, including identifying features associated with vulnerable plaques known to presage acute coronary events. Manual interpretation of IVUS, IVOCT and CCTA images demands scarce physician expertise and high time cost. This has motivated recent research into and development of artificial intelligence (AI)-assisted methods for image processing, feature extraction, plaque identification and characterization. We performed parallel searches of the medical and technical literature from 1995 to 2021 focusing respectively on human plaque characterization using various imaging modalities and the use of AI-assisted computer aided diagnosis (CAD) to detect and classify atherosclerotic plaques, including their composition and the presence of high-risk features denoting vulnerable plaques. A total of 122 publications were selected for evaluation and the analysis was summarized in terms of data sources, methods—machine versus deep learning—and performance metrics. Trends in AI-assisted plaque characterization are detailed and prospective research challenges discussed. Future directions for the development of accurate and efficient CAD systems to characterize plaque noninvasively using CCTA are proposed. Coronary artery disease is a major cause of morbidity and mortality worldwide. Its underlying histopathology is the formation of atherosclerotic plaques within the wall lining of the coronary artery tree . ChronicAtherosclerotic plaques comprise heterogeneous components such as extracellular lipids, collagen fibers (fibrous tissue), loose collagen fibers with lipid accumulation (mixed tissue) and—when chronic—compact calcium crystal deposits  . The AmeIntravascular ultrasound (IVUS) and intravascular optical coherence tomography (IVOCT) performed via catheter during invasive coronary angiography (CAG) possess high spatial resolution and signal contrast, and are the reference standards for in vivo near-field characterization of individual atherosclerotic plaques. Based on the constituents, atheromatous plaques can be stratified as stable or vulnerable. The former is characterized by heavy calcification, fibrotic tissue, and small lipid pools. In contrast, the latter contains a large lipid pool (necrotic core) with or without spotty calcification, and is covered by a thin fibrous cap that is soft in nature and prone to rupture—the thin-cap fibroatheroma (TCFA). Post-processing techniques such as virtual histology intravascular ultrasound (VH-IVUS)  can enhaNoninvasive coronary computed tomographic angiography (CCTA) can also characterize atherosclerotic plaques albeit with less fine spatial resolution than invasive IVUS and IVOCT. Of advantage, CCTA can survey the entire coronary tree within a single three-dimensional image dataset and possesses high sensitivity for calcium deposition. Coronary plaques can thus be categorized as noncalcified, partially calcified and calcified, and the severity of calcification quantitated. Using advanced analysis, lipid and fibrotic components in noncalcified plaques can also be identified .To compare manual grading systems for plaque characterization with various imaging modalities;To analyze state-of-the-art artificial intelligence (AI) techniques to characterize plaque;To discuss the results and roles of different techniques for plaque characterization; andTo highlight potential research gaps and future research directions related to plaque characterization using CAD.Manual identification and characterization of coronary plaques demands clinical expertise and high time costs. It is also dependent on image quality, which is easily affected by speckle noise. These reasons motivate the development of computer aided diagnosis (CAD) systems for automated image processing as well as coronary plaque identification and characterization on both invasive  and noniThe remainder of the paper is structured as follows: description of article selection process is in The review process was carried out based on the procedures and guidelines presented in PRISMA . We perfTo identify the most suitable and eligible articles: These articles were further filtered using inclusion/exclusion criteria. We included prospective studies that compared various imaging modalities such as CCTA, IVOCT and IVUS in the diagnosis and characterization of coronary atherosclerotic plaques. In addition, studies that employed various ML and DL to segment plaque regions from the image dataset, as well as to detect and classify coronary plaque type, were considered. We also considered clinical studies that focused on coronary plaque detection, grading, and characterization including coronary calcification. Studies related to image acquisition technique, non-coronary vascular assessment, coronary anomalies, coronary artery stenosis severity, and only animals were excluded. The selected articles comprised those with medical and technical contents see . The finSeveral invasive and non-invasive modalities are employed in clinical practice for the identification and assessment of coronary plaques , e.g., IInvasive CAG is the gold standard for coronary artery disease diagnosis based on detection of coronary lumen stenosis but is of limited utility for plaque characterization beyond outlining intracoronary thrombus associated with ruptured atherosclerotic plaque. Nevertheless, invasive CAG is a prerequisite for plaque characterization with IVUS and IVOCT. IVUS is the gold standard for assessment of the atherosclerotic coronary plaque ,20. A miMulti-slice CCTA has evolved in the past two decades to become the dominant non-invasive imaging technique for coronary artery disease diagnosis ,29, whicUsing early four-detector CT scanners, Becker et al.  showed tAI enables extraction and processing of quantitative information using human-like intelligence systems ,57,58,59As described above, IVUS, IVOCT, and CCTA are the imaging modalities that can be used to characterize coronary atherosclerotic plaques. Research and development of CAD tools for plaque characterization have relied on image datasets from private ,67,68 orMachine Learning TechniquesML uses various algorithms for the following key steps: preprocessing and segmentation; feature extraction; dimensionality reduction or feature ranking; and classification. Image preprocessing is a crucial preliminary step as it enhances the image quality or resolution using appropriate filters ,75,76. EDeep Learning TechniquesAutomated image recognition has improved tremendously since the widespread implementation of DL techniques, especially those with a convolutional neural network (CNN) architecture. CNN is composed of a series of layers, in which discriminative image features are extracted from raw data automatically ,78. In gImages obtained from different modalities may not be carry sufficient information for further processing or may be of low quality. Preprocessing helps to standardize image quality and increase image contrast. Denoising techniques include the anisotropic diffusion filter ,81, GausTo accurately detect the plaque, certain features are extracted from the image. Using the widely used gray level co-occurrence matrix (GLCM), textural features can be extracted based on the spatial relationship between pixels ,93,94. Ht-test or wrapped feature selection (WRP) t-test oron (WRP) .Plaques can be effectively categorized as calcified and non-calcified plaques using the Bayesian classifier . In 92]92], an oFor plaque classification, deep learning models such as ResNet50-v2 and DenseNet-121 from ImageNet are used for binary as well as multiclass plaque classifications . PlaquesIn , calcifiCNN-based DL techniques are commonly used for plaque classification or identification. A distribution-preserving autoencoder-based neural network (DPAE–NN) implemented on IVOCT images was found to be best suited for characterization of heterogeneous plaques . Images In this study, we present a systematic catalogue of CAD using ML and DL techniques for coronary atherosclerotic plaque characterization using various imaging modalities.p < 0.001) and sensitivity, specificity, PPV and NPV of 77%, 74%, 67% and 86%, respectively, when referenced against invasive FFR-assessed coronary artery ischemia [When referenced to invasive CAG, CCTA has sensitivity of 73–99%, specificity of 54–94%, positive predictive value of 64–92% and negative predictive value (NPV) of 83–100% for coronary artery disease detection ,130,131.ischemia . CT-baseischemia . Two metischemia ,136. Detischemia . Detectiischemia . OverallAutomatic coronary plaque detection has been studied in 61 manuscripts included in this review report: 16 manuscripts are based on CCTA, 28 based on IVUS, 16 manuscripts are based on IVOCT, and one is based on X-ray angiography.Most studies have implemented classification techniques to categorize image data sets as either containing plaque, no plaque, or mixed plaque. Different classifiers were used and the performance metrics of the classifiers reported in terms of outcomes, such as sensitivity, specificity, AUC, accuracy, PPV, and NPV. The majority of articles reported sensitivity (30 manuscripts), specificity (24 manuscripts) and accuracy (38 manuscripts) as performance metrics. Performance parameters such as AUC, PPV, and average precision were also used to assess performance. Among the classifiers, SVM was found to outperform, and was implemented in 14 articles ,124. TheIn this review, performance outcomes for plaque detection and classification using ML and DL were compared. In , the higSupervised learning and a DL algorithm were combined to form a hybrid method SVM-based CNN algorithm, which effectively characterized the image pixels into plaque and non-plaque regions. This hybrid model achieved an accuracy of 98.8%, sensitivity of 100%, and specificity of 98.70% on IVUS images acquired from seven patients . SimilarIt is observed that many studies in this field are based on IVUS. While invasive techniques are the reference standards for identifying and grading plaques and are helpful for understanding the underlying etiology of cerebrovascular disease, CCTA has emerged as the dominant noninvasive modality for studying coronary atherosclerosis. With CCTA, the identification of vulnerable plaques and their quantification relies on expert interpretation and is dependent on the degree of the interobserver variability. In addition, CCTA cannot supplant invasive techniques for more subtle plaque characteristics such as erosion and neovascularization . While 3Availability of plaque datasets: Currently there is a lack of datasets that are publicly available. Most of the CAD are developed based on private datasets with low sample numbers. Hence, it is difficult to generalize from such analyses. Another challenge is to develop a large dataset with annotated plaque severity. This process is tedious and expensive, as it requires the input of expert radiologists. Data imbalance with various imaging modalities and their levels of severity may cause overfit or underfit of the training models. In order to handle skewed data, augmentation can be utilized in all modalities. The collection of more data of the different modalities can be achieved with the help of collaborative partner hospitals and clinics.Comparative study on CAD using various modalities: There is a dearth of studies that have compared different modalities for the development of CAD to characterize plaques. Hence, a major challenge for researchers is to develop an efficient CAD to identify plaques using various imaging modalities, such as CCTA and IVUS images of the same patient. Investigators should concentrate on obtaining an automatic grading system using noninvasive imaging technique such as CCTA. This should then be compared with the expert grading system to produce a generalized CAD for plaque categorization. Finally, the CAD should be fast, noninvasive, inexpensive, and accurate, so that it can be used anywhere to obtain plaque deposition results without the need for expert involvement.CAD in the assessment of prognosis: Though several CAD are developed to detect and characterize plaques, none of the studies have reported on prognosis as well as responsiveness to treatment. Hybrid methods, such as a combination of ML and DL based approaches, can be further explored to develop CAD that correlates initial plaque deposition before and after treatment, as well as with long-term clinical outcome.Generally, physicians or radiologists assess plaque deposition and its grading by using predefined image frames on IVUS, IVOCT and CCTA, and the results may vary from expert to expert based on their expertise in the various modalities, hence delaying treatment. Therefore, the diagnostic assessment of patients is typically performed by tertiary hospitals that are equipped to obtain and interpret the required images. It is thus difficult for geographically separated patients to obtain high-level diagnosis and treatment in a timely manner. In the future, cloud-based wireless healthcare systems can provide improved diagnostics (please refer to The present review has been carried out based on manuscripts written in English. Other language manuscripts were not included during the review process;The current review process included a plaque grading system using various modalities, and analysis of various AI algorithms to develop CAD for plaque categorization. However, review on grading during plaque deposition and after treatment was not given substantial consideration;The specific reasons to select the algorithms based on AI were not mentioned. It was also unclear whether the proposed CAD can improve the survival of patients.Some limitations of the study are given below:This review summarizes both medical and technical manuscripts for plaque characterization. A total of 61 medical manuscripts were used to understand the manual plaque grading schemes with various modalities and 61 technical papers were analyzed to understand the application of AI algorithms in the prediction of plaque deposition, classification, and detection. These methods were summarized and discussed along with various aspects of research challenges. Experimental results show that AI algorithms using ML and DL based methods have merits for identifying plaques, and can be used as a valuable resource in the medical decision-making process. In the future, these AI methods can be exploited to achieve better results by addressing future challenges and implementing the AI models in real clinical trials.


This observation fueled the widespread hypotheses that these properties extend to living neurons and that reduced stability of microtubules represents a major disease-driving event induced by pathological forms of tau in Alzheimer’s disease and other tauopathies. Accordingly, most research efforts to date have addressed this protein as a substrate, focusing on evaluating how specific mutations, phosphorylation, and other post-translational modifications impact its microtubule-binding and stabilizing properties. In contrast, fewer efforts were made to illuminate potential mechanisms linking physiological and disease-related forms of tau to the normal and pathological regulation of kinases and phosphatases. Here, we discuss published work indicating that, through interactions with various kinases and phosphatases, tau may normally act as a scaffolding protein to regulate phosphorylation-based signaling pathways. Expanding on this concept, we also review experimental evidence linking disease-related tau species to the misregulation of these pathways. Collectively, the available evidence supports the participation of tau in multiple cellular processes sustaining neuronal and glial function through various mechanisms involving the scaffolding and regulation of selected kinases and phosphatases at discrete subcellular compartments. The notion that the repertoire of tau functions includes a role as a signaling hub should widen our interpretation of experimental results and increase our understanding of tau biology in normal and disease conditions.Over four decades ago,  Such dynamics likely impact tau’s availability to interact with kinases and phosphatases on and off the MT surface . Conversely, neuronal survival and homeostasis depend on retrograde FAT (rFAT) of specialized MBOs bearing neurotrophins and degraded materials from peripheral compartments towards the neuronal soma. In the mature brain, aFAT and rFAT are mainly driven by the multi-subunit motor proteins kinesin-1  and cytoplasmic dynein, respectively. Both directions of FAT are regulated, in part, by signaling pathways controlling phosphorylation of these microtubule-based motors  along axons, a cellular process referred to as fast axonal transport (FAT), is critical for appropriate maintenance of neuronal connectivity and survival Black, . The corex vivo preparation uniquely suited for the study of FAT mechanisms , causing aberrant exposure of the N-terminus . These antibodies selectively recognize an epitope within PAD that is exposed in a conformation-dependent manner  or long-term depression (LTD), respectively . Furthermore, PP1 activity is required for the successful execution of pre-mRNA splicing  mRNA translation at the axon-glia interface  to convert it into phosphatidylinositol -triphosphate (PIP3). PIP3 recruits AKT to the cell membrane where it is activated and phosphorylates numerous kinase substrates  that in turn regulate a multitude of cellular processes. Phosphatase and tensin homolog (PTEN) dephosphorylates PIP3, providing a mechanism to terminate insulin-dependent signaling.Signaling pathways activated by insulin play an important role in the maintenance of neuronal function and peripheral metabolism  to regulate cell differentiation, survival, and death (Keshet and Seger, Various reports documented modulatory effects of tau on neurotrophin-based activation of MAPK signaling . For exaThe long-held hypothesis that stabilization of MTs represents tau’s primary biological function is being increasingly challenged by a growing body of independent experimental evidence. Continuing to challenge this dogma will help expand our interpretation of such evidence and advance our understanding of tau biology in health and disease conditions. For example, the ability of tau to interact with MTs may reflect just one of several features enabling a much more diverse set of functions by this protein, such as the regulation of phosphorylation-based signaling pathways in axons and other MT-rich subcellular compartments. On the other hand, MT-independent interactions with additional proteins may facilitate similar functions in modulating pathways in other compartments featuring few or no MTs. This notion appears consistent with tau’s widespread subcellular distribution, its highly dynamic conformational flexibility, and the numerous kinase and phosphatase binding partners identified thus far. Based on these characteristics, we posit that tau functions as a signaling hub, regulating a wide set of cellular processes through the modulation of selected kinases and phosphatases. This function in turn appears to be impacted by specific modification events in tau, including mutations, phosphorylation, truncation, and aggregation, which could have important implications for physiological and disease states. A more dynamic perspective of tau as a multifaceted protein involved in the control of intracellular signaling may also provide a novel framework for the development of therapeutic approaches to treat tauopathies.RM, BC, MA, SB, GM, and NK each contributed to writing and editing this review. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Pancreatic cancer is the most lethal solid malignancy, and the number of patients with pancreatic cancer is increasing. Systemic chemotherapies are often ineffective for such patients, and there is an urgent need for personalized medicine. Unlike other types of cancer, personalized treatments for pancreatic cancer are still in development. Consequently, pancreatic cancer is less sensitive to anticancer drugs and is often refractory to common treatments. Therefore, advances in personalized medicine for pancreatic cancer are necessary. This review examined advances in personalized medicine for pancreatic cancer, including the use of endoscopic ultrasound (EUS)-guided sampling. EUS-guided sampling is widely used for diagnosing pancreatic tumors and is expected to be applied to sampled tissues. Additionally, there has been an increase in clinical research using EUS-guided sampling. The combination of precision medicine using genomic testing and pharmacological profiles based on high-throughput drug sensitivity testing using patient-derived organoids is expected to revolutionize pancreatic cancer treatment. The number of patients diagnosed with pancreatic duct tumors is increasing annually. Currently, pancreatic ductal adenocarcinoma (PDAC) is the seventh leading cause of cancer death worldwide, with an estimated 432,000 new cases and 459,000 deaths yearly . PancreaSince chemotherapy is administered uniformly based on the results of clinical trials, therapeutic effects vary from patient to patient. This may be because the efficacies of anticancer drugs differ due to genomic differences, even in patients with the same cancer . TherefoEndoscopic ultrasound (EUS)-guided sampling, which can reliably collect tissue from malignant tumors of the bile duct and pancreas before administering chemotherapy, has been developed to facilitate the tissue collection of malignant tumors since the 1990s . With thNeoadjuvant chemotherapy has been common for operable or borderline PDAC . In the Next-generation sequencing (NGS) that collectively captures cellular activities using conventional methods, such as polymerase chain reaction, Western blotting, and enzyme-linked immunosorbent assays that examine a single gene or protein have been proposed for analyzing tumor tissue . The invHere, we summarize recent studies on personalized medicine for PDAC and show recent findings regarding the application of EUS-guided sampling to personalized medicine.Traditional anticancer drugs act on all rapidly dividing normal cells and cancer cells. Therefore, the approach to treatment is shifting to genetically modified strategies that identify potential therapeutic targets with enhanced specificity. However, it is necessary to conduct genotype-matched clinical trials, such as basket and umbrella studies, to confirm the usefulness of cancer precision medicine ,18. SeveGenomic sequencing assays for tumor analysis are a recent development in the field of cancer treatment ,24. The In a recent large cohort study of 10,000 cancer patients using the MSK-IMPACT platform, 37% of patients had actionable genetic changes. However, since then, only 11% have been enrolled in genotype-matched clinical trials . ConversThere are some unresolved issues regarding genomic testing for PDAC in clinical practice. It is often possible to obtain sufficient surgical specimens and biopsy samples for other tumors, however the difficulty in obtaining pancreatic tumor samples is a major problem. EUS-guided sampling has been used to collect tumor samples from the pancreas since the 1990s and has become widely used as the first choice for sampling PDACs . EUS-guided fine-needle aspiration (FNA) was first described in 1993 by Vilmann et al. as a technique to collect samples for smear cytology . Later, Currently available cancer genomic profiling tests, such as the FoundationOne CDx , which is a qualitative NGS-based in vitro diagnostic test that uses high-throughput hybridization-based capture technology to detect large amounts of genomic information, usually requiring 10–500 ng of DNA. However, it is still unclear whether EUS-guided sampling is the optimal method for collecting tumor samples for genomic sequencing of PDACs ,40. For Successful molecular biological analysis from fixed formalin specimens of cell specimens collected by EUS-FNA was reported in 2010 by Fujita et al. . In 2012KRAS, CDKN2A, TP53, and SMAD4 have been identified as the major driver genes for PDAC, detection rates for other genetic alterations have been reported to be very low [PDAC has been reported to have only a few druggable gene alterations. Two large cohort studies have investigated the status of PDAC mutations using genomic analysis. Although very low ,49,50. Avery low .BRCA1/2, PALB2, FANC, and ATM has been reported to be a promising therapeutic strategy for PDACs that are sensitive to poly (ADP-ribose) polymerase (PARP) inhibitors and platinum [Precision medicine for PDAC is currently still under development. For example, targeting genetic alterations associated with homologous recombination deficiencies such as platinum . The PARplatinum . The furAlthough gene mutations promote tumor progression, some reports have suggested that they may be less associated with chemotherapy resistance ,54,55. OPrecision medicine approaches for PDAC are challenging because of the short median survival of patients with metastatic PDAC. Furthermore, sensitivity testing is difficult for the following reasons: (1) difficulty in establishing cell culture ; (2) theTo evaluate the drug sensitivity of pancreatic malignancies, it is necessary to establish a more efficient method for culturing cancer cells. The cancer stem cell hypothesis states that there is a small subpopulation of cells in tumors with a capability of self-renewal and tumorigenicity. However, attempts to culture those cancer stem cells have been unsuccessful. In 2007, it was reported that colon cancer stem cells could be cultured using the sphere culture method , but thePatient-derived organoids (PDOs) have the potential to be used as important tools for personalized cancer treatment . Basic cWhether the clinical implementation of PDOs for drug sensitivity tests is useful has not been fully evaluated for PDAC. However, irradiation/drug tests using rectal cancer PDOs were reported to have an accuracy of 84.4% in the clinical treatment course. In most rectal cancer cases, PDO response analysis was completed in less than four weeks, enabling treatment recommendations to be generated within a clinically meaningful time frame . If thesOnly ~20% of patients with PDAC are eligible for surgical intervention , and sufRecently, there have been increasing numbers of reports on how to effectively collect specimens to establish organoids . FNB-basMethods for establishing PDAC organoids have been reported by multiple research groups. In the field of PDAC-related organoid research, the first organoid was generated from mouse pancreatic tissues ,64. FollThere have also been several reports on how to generate PDOs from PDAC specimens collected by EUS-guided sampling. The Tuveson research group ,63, whicThe niche factors, which are essential for the growth and survival of PDOs are determined by each organoid culture. Normal human pancreas-derived organoids require EGF, BMP inhibitor, TGF-beta receptor inhibitor, Wnt3A, and Rspondin1, while some PDAC organoids can still propagate without some of these factors due to genetic aberrations. Despite the lack of genetic abnormalities in canonical Wnt signaling pathways, comportment of the PDAC organoids could still expand in Wnt or R-spondin free environments. Seino et al. proposed the classification of PDACs based on Wnt signaling dependency and revealed that PDACs could potentially become more malignant, with independence from the stem cell niche. Our understanding of the minimum culture condition in which some of the niche factors are depleted enables us to predict the existence of genetic changes and the malignant status of original tumors.® 3D cell viability assay, Promega) is reported to be used to determine the number of viable cells in 3D cell culture based on quantitation of the ATP present, a marker for the presence of active cells. Furthermore, an ATP-independent assay method to measure the activity of dead-cell protease, which is released from cells that have lost membrane integrity , was also used in several reports. The CytoTox-Glo™ assay relies on the properties of recombinant luciferase, which uses aminoluciferin as a substrate to generate a stable luminescent signal.First, we will discuss the high-throughput method, which has rapidly promoted the practical application of actual drug screening testing. High-throughput screening (HTS) is now being used for basic and applied research in academia . It compIn principle, organoids are suitable for HTS, but the technical constraints and extensive manipulation required by current methods have hampered progress toward simple clinical applications. Therefore, various simple methods have been developed, and methods for reducing costs have been reported. Fluorometric methods to quantitatively assess cell death using propidium iodide and Hoechst fluorescence in 3D in a plate reader have been reported to have significantly improved analysis time, as well as ease of use, compared to other established methods . AnotherThe proper timing for assaying cell death after drug administration remains unknown. Most reports have examined cell death approximately 1–5 days after drug administration. There has been not standardized method for HTS using PDOs, but it is expected that a consensus will soon be reached. There have been few actual reports on PDAC cases , and furThe feasibility of a drug screening study using PDOs was first shown in metastatic, heavily pretreated colorectal and gastroesophageal cancer patients recruited in phase I/II clinical trials. They showed PDOS drug screening with 100% sensitivity, 93% specificity, 88% positive predictive value, and 100% negative predictive value in forecasting response to targeted agents or chemotherapy in patients . SeveralWolff et al. followed one patient with PDAC for five years, with clinical records, pathology, dynamic evolution of molecular and cellular changes, chemotherapy and targeted therapy with PDOs culture over time, and the response to treatment with immunotherapy . AlthougIn addition, Frappart et al. also reported a drug screening study for 22 anticancer drugs in patients with PDAC . In the As described above, PDAC drug screening has not yet been totally successful. This may be related to the lack of effective anticancer drugs and the microenvironmental factors of tumors exhibiting strong fibrosis. In addition, as described in the previous section, a method for determining the therapeutic effect of high-throughput drug screening for PDAC organoids has not been implemented in clinical practice. To implement PDOs drug screening in actual clinical practice, a large number of studies and development of more effective anticancer agents are required in metastatic PDAC cases.The amount and quality of tissues extracted using EUS-guided sampling are frequently insufficient for histopathological diagnosis, despite improved puncture techniques and needles. New diagnostic methods in addition to EUS-guided sampling, such as microRNA-based tests , confocaPersonalized medicine for PDAC is still in development. Consequently, PDAC has a low sensitivity to anticancer drugs and is typically intractable. However, advances in personalized cancer care for PDAC will allow improved chemotherapy selection and more effective patient selection, as well as facilitate the identification of patients in whom chemotherapy may be ineffective, thus avoiding unnecessary treatment. It is expected that the combined use of gene profiling based on genetic testing and pharmaceutical profiling using HTS and PDOs will revolutionize PDAC treatment.We recommend the use of FNB needles to collect DNA for genomic testing because of its ability to extract more tissue. Clinical studies on the diameter of needles, number of needle passes, and processing techniques are needed. Additionally, EUS-guided sampling is expected to help identify changes in tumor sensitivity over time and identify patient-specific response to chemotherapy. EUS-guided sampling has few complications and allows for repeat tissue sampling. Therefore, it is expected that regular PDO preparation using EUS-guided sampling during the course of treatment will improve treatment accuracy.