Treatment resistant schizophrenia (TRS) is characterized by a lack of, or suboptimal response to, antipsychotic agents. The biological underpinnings of this clinical condition are still scarcely understood. Since all antipsychotics block dopamine D2 receptors (D2R), dopamine-related mechanisms should be considered the main candidates in the neurobiology of antipsychotic non-response, although other neurotransmitter systems play a role. The aims of this review are: (i) to recapitulate and critically appraise the relevant literature on dopamine-related mechanisms of TRS; (ii) to discuss the methodological limitations of the studies so far conducted and delineate a theoretical framework on dopamine mechanisms of TRS; and (iii) to highlight future perspectives of research and unmet needs. Dopamine-related neurobiological mechanisms of TRS may be multiple and putatively subdivided into three biological points: (1) D2R-related, including increased D2R levels; increased density of D2Rs in the high-affinity state; aberrant D2R dimer or heteromer formation; imbalance between D2R short and long variants; extrastriatal D2Rs; (2) presynaptic dopamine, including low or normal dopamine synthesis and/or release compared to responder patients; and (3) exaggerated postsynaptic D2R-mediated neurotransmission. Future points to be addressed are: (i) a more neurobiologically-oriented phenotypic categorization of TRS; (ii) implementation of neurobiological studies by directly comparing treatment resistant vs. treatment responder patients; (iii) development of a reliable animal model of non-response to antipsychotics. Schizophrenia is among the most debilitating disorders in psychiatry, with great impairment of social and individual functioning . AlthougSchizophrenia treatment may be completely or partially unsuccessful for multiple clinical reasons, including (but not limited to) the possibility that patients may be receiving a suboptimal dose of antipsychotics, may suffer from comorbid substance misuse, or may be under concurrent use of other prescribed medicines or a concomitant physical illness that may negatively impact antipsychotic treatment. Genetic or iatrogenic variations in antipsychotic pharmacokinetics may also lead to inadequate blood levels of the drug and ineffective drug concentrations at the site of action ,5,6,7. TIn a substantial number of non-responder patients, however, clinical variables can be reasonably excluded, and no pharmacokinetics variations can be suspected. These patients may putatively represent treatment resistant cases due to pharmacodynamic causes . NonetheSchizophrenia has been prominently, albeit not exclusively, regarded as a dysfunction of central dopamine neurotransmission ,10,11. FRemarkably, all antipsychotic drugs share a variable degree of dopamine D2 receptor (D2R) blockade, even if with different affinities and acting as antagonists or partial agonists . More spBased on these considerations, multiple neurotransmitters have also been implicated in the pathophysiology of schizophrenia  and shouDespite the possible involvement of other neurotransmitter systems, dopamine dysfunction is still considered the most relevant common pathway leading to schizophrenia , and allA comprehensive review of the literature was carried out through Medline/Pubmed, Embase, and Scopus databases. Keywords were searched in the Title/Abstract fields; no date restriction was set; only publications in the English language were included. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines  were folThe systematic review of reports yielded 101 studies that met inclusion criteria. Given the large methodological heterogeneity of studies included, ranging from preclinical cell culture paradigms to animal models of non-response to antipsychotics to human neuroimaging reports on dopamine system dysfunctions in treatment resistant schizophrenia patients, we were unable to carry out a quantitative synthesis of evidence.The lack of a well-defined theoretical framework  in multiple studies also prevented the possibility of proceeding with a hypothesis-driven approach, i.e., reporting different hypotheses to explain dopamine-related underpinnings of TRS and listing studies confirming or rejecting each hypothesis.n = 9 reports); 2. D2 receptor dynamics related (n = 37 reports); 3. dopamine presynaptic alterations related (n = 35 reports); 4. dopamine postsynaptic site alterations related (n = 20 reports).In the attempt to summarize the data, we provided a qualitative synthesis of reports, subdividing them into four large putative neurobiological sites of dopamine-related non-response to antipsychotics, namely: 1. dopamine genes related  compared to Ins/Ins subjects -(+)-PHNO, which is a D2R and D3R agonist reported to bind D2High [11C]-(+)-PHNO failed to find any difference in D2R binding between antipsychotic-naïve patients, clinical-high-risk subjects, and healthy controls, both in resting conditions or under a cognitive task considered increased striatal dopamine release -(+)-PHN O is regarded to have a 50-fold higher affinity to D3RS over D2Rs -I-2-CH3O-N-n-propylnorapomorphine ([11C]MNPA) was used as the radioligand, since it has an almost identical affinity to D2RS and D3Rs and may be more suitable to study D2High density [11C]MNPA to [11C]raclopride binding in the putamen of schizophrenia patients was higher than in controls . I. I116]. High, as a consequence of impaired negative cooperativity, may prevent antipsychotics to revert striatal hyperdopaminergia and cause a non-response. However, there is no evidence to date that treatment resistant patients have a larger proportion of D2R dimers than responder patients.D2Rs have been described to exist as both monomers and dimers in brain tissues . In humaIntriguingly, it has been recently reported that distinct D2R antagonists may differently affect D2R dimerization levels, which may have profound effects on downstream postreceptor signaling and may putatively contribute to explaining differences in response to antipsychotic agents  118]..118].D2Rs have been supposed to interact, either physically or functionally, with multiple dopaminergic and non-dopaminergic receptor subtypes ,122. Theq-mediated intracellular signaling leading to increased release of intracellular calcium -2-aminopropane (DOI) for 5-HT2ARs , demonsteceptors . Remarka agonist , indicat agonist  and suggst IBZM (which binds primarily to postsynaptic D2Rs) and [123I]FP-CIT , a reduced radioligand binding in the caudate-putamen of healthy subjects carrying the T allele compared to homozygous G allele carriers has been found epidepride to evaluate D2/D3R binding potential in antipsychotic-naïve first episode schizophrenia subjects at baseline and after a 3-month treatment by risperidone or zuclopenthixol. No significant association was found between D2/D3R occupancy in extrastriatal sites and improvement in the Positive and Negative Syndrome Scale (PANSS) positive subscale score at the 3-month follow-up evaluation IBZM binding to striatal D2Rs was also significantly higher in schizophrenia patients compared to healthy controls after dopamine depletion by alpha-methyl-para-tyrosine iodobenzamide showed that there was a significant negative correlation between low striatal D2R binding potential at baseline (which was considered to indicate higher synaptic dopamine levels) and amelioration of positive symptoms after a 6-week treatment with amisulpride in antipsychotic-naïve schizophrenia patients [In agreement with this earlier study, a further SPECT study with [patients . Respondpatients . Moreovepatients . Specifipatients . In partpatients . While tpatients , at leasAn intriguing and somewhat different point of view has been recently suggested ,186, basSome studies have investigated the effects of antipsychotics on dopamine synthesis capacity in schizophrenia patients based on the consideration that the antipsychotic effect may not be entirely attributed to the blockade of postsynaptic D2Rs. Indeed, similar occupancy of D2Rs in the striatum has been observed in both responder and non-responder patients in an early study , suggestAccording to this idea, in a PET study on nine schizophrenia patients, 5-week haloperidol therapy caused a significant decrease in DOPA decarboxylase relative activity in the caudate, the putamen, the thalamus, and the orbital and frontal cortices . These dData from human studies are reinforced by preclinical observations. Repeated administration of antipsychotics to rodents is known to trigger depolarization block, i.e., a state of dopamine neuron inactivation . In non-As higher dopamine synthesis capacity has been associated with better response to antipsychotics , the posTaken together, these data suggest that non-response to antipsychotics may be due to the fact the psychotic symptoms in these patients are not caused by elevated presynaptic dopamine synthesis . Since pAn interesting approach has been to evaluate whether treatment resistant patients had higher heterogeneity in striatal dopamine function compared to treatment resistant patients, a feature investigated in a subgroup meta-analysis of interindividual variance in multiple outcomes of dopamine function . AlthougSubsequent data suggested that treatment resistant patients may have normal striatal dopamine synthesis capacity but elevated anterior cingulate glutamate levels , as oppoIndirect support for these findings comes from a recent study on gene expression and protein levels of dopamine-related molecules in the postmortem midbrain of schizophrenia patients. In this study, DAT mRNA expression was found significantly decreased in schizophrenia patients compared to controls, in agreement with the hypothesis of a presynaptic dysregulation leading to striatal hyperdopaminergia in schizophrenia . HoweverIn partial agreement with these data, in a small sample of schizophrenia patients with comorbid mixed substance dependence, a blunting rather than an increase in presynaptic striatal dopamine release has been observed . These pHuman studies on dopamine-related postsynaptic mechanisms of response to antipsychotics are currently limited by methodological challenges. Preclinical findings have pointed to several potential molecular processes that affect striatal dopamine neurotransmission, and that may be implicated both in the mechanism of action of antipsychotic agents and in the failure of their efficacy, including disrupted mammalian target of rapamycin (mTOR) Complex 2 (mTORC2) signaling ; abnormaD2R signaling strength may be modulated by internalization and degradation processes. A deficit in internalization/degradation processes may predispose to aberrantly high D2R-mediated neurotransmission, and theoretically, to the lack of response to antipsychotic agents .−/−NCAM mutant mice have been associated with increased membrane expression of D2Rs, excessive D2R-mediated postsynaptic signaling, enhanced locomotor activity in response to dopamine-agonists, and lower response to a D2R antagonist in terms of locomotor activity reduction [The neural cell adhesion molecule (NCAM) is a transmembrane postsynaptic protein that interacts with D2Rs via its third intracellular loop . Geneticeduction . Therefors1801028 polymorphism of the DRD2 gene corresponds to a Ser/Cys alternative phenotype in the aminoacidic sequence located in the area of NCAM-D2R interaction. This polymorphism has been considered a risk factor for schizophrenia, although recent meta-analysis did not confirm this view [311Cys mutants showed a 35% decrease of D2R binding to NCAM [Cys allele carriers had significantly higher PANSS total and subscale scores compared to Ser/Ser patients [Notably, the his view  and has his view . Compare to NCAM , which mpatients . Since pStriatal D2Rs have been considered to exert part of their downstream action in a cyclic adenosine monophosphate (cAMP)-independent manner by the formation of a signaling complex comprising AKT1, protein phosphatase 2 (PP2A), and β-arrestin-2 . FormatiIndeed, AKT1 protein levels were found to be significantly reduced in lymphocyte-derived cell lines and in the frontal cortex and hippocampus of postmortem brains of schizophrenia patients compared to controls . AdditioD2R knock-out mice [2A and 5-HT1ARs increased and decreased GSK-3 activation, respectively [2ARs and, to a lesser extent, agonists at 5-HT1ARs, GSK-3 may represent a site of convergence of most antipsychotic actions. Remarkably, antipsychotics appear to exert different actions on these molecules. Indeed, levels of phosphorylated AKT1 in the rat frontal cortex rapidly returned to baseline after an acute haloperidol exposure, while AKT1 remained phosphorylated after an acute clozapine treatment [Notably, antipsychotic treatments significantly increased AKT1 and GSK-3 phosphorylation in the rodent brain ,217, posout mice . Moreoveectively . Since mreatment .i/o protein signaling [It has been demonstrated that a series of typical and atypical antipsychotics share the property to potently antagonize β-arrestin-2 recruitment by D2Rs after their stimulation by quinpirole . Interesignaling , suggestNR1 subunit knock-down mice [A preclinical study has described the generation of unprecedented β-arrestin-biased D2R ligands . These cown mice , and twoown mice .i/o [All these reports indicate that the β-arrestin-2/AKT1/GSK-3 pathway may represent a major target for existing and future antipsychotics . Indeed,i/o . PutativScaffolding proteins are enriched at the postsynaptic density (PSD) of medium-sized spiny neurons, an ultrastructure located in the proximity of membrane surface and whose main biological function is to integrate receptor-mediated signaling with intracellular effectors and to mediate cross-talk among different transduction systems . DespiteHomer1a inducible transcript has been found selectively induced by D2R antagonism [Homer1 gene expression according to the distinct gene isoform, brain region, antipsychotic type, and administration timing [Homer1a has been used as a marker of glutamatergic activity to provide network maps of brain functions in relation to antipsychotic administration [Striatal expression of the tagonism , and antn timing ,236,237.stration .Homer1 sequence, i.e., rs2290639, has been significantly associated with psychotic symptoms measured by PANSS and response to four-week antipsychotic treatment in a population of schizophrenia patients [At least one polymorphism in the patients .Homer1a may represent a rapidly-induced tool to transiently impair this molecular cross-talk [Experimental studies suggest that constitutive Homer proteins may facilitate the cross-talk between dopamine and glutamate receptors signaling pathways, while the inducible isoform oss-talk  and to poss-talk . These mTo date, only a few studies have provided a direct comparison of responder vs. non-responder schizophrenia patients relative to biological underpinnings of the lack of response to antipsychotics. The great part of studies providing such a comparison is from a neuroimaging perspective. Many others do not provide a direct comparison; however, they report data that may be informative of the mechanisms implied in response/non-response to antipsychotics . Only a As a result of all these considerations, it should be concluded that research on the field of neurobiological underpinnings of TRS still has a long way to go. Nonetheless, many studies have investigated clinical differences in treatment resistant vs. treatment responder patients ,242,243,It has been proposed to re-classify schizophrenia patients according to their response to antipsychotics in three different groups  . Such a Howes and Kapur  have proNonetheless, these reports are paving the way for a reconceptualization and a critical appraisal of research strategies used (and to be used) for studying TRS. Recent attempts have been made to unify the nomenclature, to provide operative criteria to define non-response to antipsychotics, and to delineate a distinct TRS syndrome . This atHigh density [High radioligand has not been manufactured yet. Remarkably, the difference in density of either the whole pool of D2Rs or the subset of D2High has not been investigated in schizophrenia responders vs. non-responders. Indeed, the compensatory increase in postsynaptic D2R levels and/or the enhanced shift of D2Rs to high-affinity states by long-term antipsychotic treatments are regarded as the main pathophysiological mechanisms of the acquired TRS-subtype known as antipsychotic-induced supersensitivity psychosis [High receptor proportion (as well as the possibly jointed phenomenon of increased D2R dimer density) have been demonstrated in the striatum of animal models of dopamine supersensitivity [Given the limitations described in the previous paragraph, there is no unitary theory to explain the lack of response to antipsychotics in schizophrenia patients. D2R-related mechanisms have been mostly investigated in preclinical or ex-vivo settings, therefore, the generalizability of these findings is challenging. The main theoretical concept is that non-response to antipsychotics may depend on abnormally high D2R density or functioning. However, to date, there is no direct evidence to confirm this hypothesis. It should also be remarked that no reports have compared responder and non-responder patients, while studies investigating the molecular events linked to response to antipsychotics were carried out in animal models of the disease. Despite early claims on the increased density of D2Rs in the brains of schizophrenia patients , subsequ density ,93,95, psychosis ,101. Agasitivity , whereasq-mediated second messenger pathway, which has been reported to be selectively targeted by clozapine [Treatment resistance may be due to aberrant density/functioning of D2R-containing heterocomplexes, which may preclude antipsychotic interaction with the receptor or may activate unique second messenger cascades that are not functionally targeted by these agents. In recent years, the possibility that D2Rs may form heteromeric complexes with D1Rs has received great attention . D1/D2R lozapine , therebylozapine . The actHowever, unlike the above-mentioned molecular complexes, Su and co-workers have demonstrated that the DISC1/D2R heterocomplex is enriched in postmortem brains of schizophrenia individuals compared to controls , renderiShort and D2Long variants since there is some evidence that different antipsychotics may differentially target these isoforms [Short/Long isoforms in regulating the response to antipsychotics in humans, future studies should be aimed at evaluating possible pathophysiological mechanisms of antipsychotic resistance involving these isoforms, including (1) increased levels or biological actions of D2Short receptors, which may be in agreement with observations of Demjaha et al. [Long receptors, which may be consistent with the hypothesis that non-response may depend on postsynaptic mechanisms and may in part comply with observations on increased high-affinity D2R states in schizophrenia and possibly in antipsychotic non-responder; or (3) an imbalance between these two receptor isoforms.Another interesting field of research is represented by putative modulation of antipsychotic efficacy by the D2isoforms ,150, posa et al.  on lowerResearch on putative postsynaptic mechanisms of non-response to antipsychotics is still in its early steps. Defective D2R internalization, aberrant D2R-mediated downstream signaling, and altered postsynaptic scaffolding protein-mediated glutamate-dopamine interactions are all putative mechanisms that have been proposed to affect D2R-mediated signaling, and that could theoretically predispose to the lack of response to antipsychotic agents. However, most of the evidence is in preclinical settings. However, direct evaluation of treatment resistant patients or comparison between responder and non-responder patients is still lacking.As a summary of findings, there are multiple intriguing theoretical mechanisms that may explain antipsychotic resistance as an effect of striatal D2R-dependent molecular events. However, to date, no studies have investigated these mechanisms in treatment resistant patients. Additionally, very few studies have been conducted on extrastriatal D2Rs and their relationship with antipsychotic efficacy. Dysfunctional D2R-dependent molecular events may play a role in hyperdopaminergic-based forms of antipsychotic resistance.Antipsychotic efficacy may be linked to an efficacious reduction of dopamine synthesis capacity. Indeed, clinical studies have demonstrated that prolonged antipsychotic treatment reduces the activity of the DOPA decarboxylase enzyme , which iAccording to these suggestions, it has been observed that DOPA uptake levels in treatment resistant patients were lower than in responder patients and similar to non-affected subjects ,192. It These elements support the proposed hyper vs. normodopaminergic schizophrenia dichotomy , with thHowever, it is not clear whether all conditions of TRS may be inscribed within the so-called type B schizophrenia. Some conditions may depend on an extremely high hyperdopaminergic state, possibly due to aberrant D2R-mediated and/or post-D2R-mediated mechanisms (see infra). Moreover, as a substantial part of treatment resistant patients is not responsive to clozapine also, it has been proposed that clozapine-resistant patients may be afflicted by a disease with even more different neurobiology than clozapine responder TRS . Namely,Research on the neurobiology of TRS should start from three key points:(1) better definition of TRS.(a)How should patients afflicted by dopamine supersensitivity-based TRS be classified? Put another way: may it be useful to parse out acquired TRS from conditions in which antipsychotic resistance occurs since the first episode of psychosis ,97?(b)Should patients with non-responsive non-positive symptoms be included within TRS? Since antipsychotics are not considered to have an efficacious impact on negative and/or cognitive symptoms , patient(c)Should non-response to antipsychotics be considered a trans-diagnostic condition rather than limited to schizophrenia? In fact, antipsychotic agents are not intended to treat schizophrenia as a whole but only to ameliorate positive symptoms (or more conservatively to ameliorate those symptoms that derive from high D2R-mediated transmission). Excluding affective psychoses, whose treatment also includes non-antipsychotic agents, the field of non-affective psychoses exhibiting positive symptoms is not merely restricted to schizophrenia. Therefore, the definition of TRS may be excessively narrow and other definitions and inclusion criteria should be provided, e.g., “treatment resistant positive symptom syndrome” which includes patients suffering from non-affective psychosis whose positive symptoms are not ameliorated by antipsychotics.Although some efforts have been made in this direction, there is still much to do. While the distinction between responders to conventional antipsychotics, responders to clozapine, and non-responders to clozapine may have large clinical utility , some ch(2) Carefully designed trials should be carried out to directly compare treatment resistant and treatment responder patients on multiple neurobiological measures, including differential genetic background, mRNA expression patterns, signaling pathways, proteomic and metabolomic events, and functional brain networks. Indeed, there are growing arguments that responder and non-responder patients may suffer from distinct neurobiological lesions  and diff(3) Animal models of antipsychotic resistance with at least face and construct validity should be developed, in order to allow more focused molecular research on the underpinnings of treatment resistance.


Recently, research on short text topic models has addressed the challenges of social media datasets. These models are typically evaluated using automated measures. However, recent work suggests that these evaluation measures do not inform whether the topics produced can yield meaningful insights for those examining social media data. Efforts to address this issue, including gauging the alignment between automated and human evaluation tasks, are hampered by a lack of knowledge about how researchers use topic models. Further problems could arise if researchers do not construct topic models optimally or use them in a way that exceeds the models’ limitations. These scenarios threaten the validity of topic model development and the insights produced by researchers employing topic modelling as a methodology. However, there is currently a lack of information about how and why topic models are used in applied research. As such, we performed a systematic literature review of 189 articles where topic modelling was used for social media analysis to understand how and why topic models are used for social media analysis. Our results suggest that the development of topic models is not aligned with the needs of those who use them for social media analysis. We have found that researchers use topic models sub-optimally. There is a lack of methodological support for researchers to build and interpret topics. We offer a set of recommendations for topic model researchers to address these problems and bridge the gap between development and applied research on short text topic models.The online version contains supplementary material available at 10.1007/s10462-023-10471-x. Social media disrupted the cultural, media and political landscape in new and unexpected ways, bringing with it new and interesting research opportunities to study social phenomena. However, social media is dynamic and both its form and effect. Societal norms, consumer behaviour, journalistic practices and media organisational strategies are rapidly evolving within these complex virtual environments.As the online and offline world become further intertwined, researchers require new ways to study online social phenomena concerning offline situational contexts. Given that traditional data collection and analysis methods are unable to scale to meet the demands of social media data (SMD), these researchers have turned to computational methods to collect and analyse this data. One of these methods, topic modelling, has become popular with researchers looking to leverage SMD to study a phenomenon of interest  of 189 recent articles that apply topic modelling to short text SMD, including a critical analysis of 99 of these articles.The methodological contribution of this research is to the broadening debate about scientific rigour in NLP, such as the importance of user-orientated research directions, contribution to model development and topic model evaluation. By identifying the benefits and pitfalls that may exist for those using these tools, we can provide a basis to improve the use of these models by applied researchers to analyse social media data. This methodological contribution has been derived from a synthesis of the literature resulting in a set of recommendations for developers covering three dimensions—Approaches, user knowledge, and research advancement. Guidance on approaches encourages developers to become familiar with the aims of the user and the methodologies into which they are building topic modelling. A key recommendation is to adopt an application-driven design where utility is demonstrated by case studies informed by subject matter experts. Recommendations focused on user knowledge aim to bridge the research (and knowledge) gap between empirical and applied works to reduce the amount of ’guess work’ users undertake. Practical steps that, if taken, will support this aim are highlighted and include increased transparency about experimental settings, basing development on application needs rather than just ML problems, and engaging with the applied literature. The final set of recommendations addresses how developers can support research advancements in ML and those disciplines in which topic models are used. For instance, there is a pressing need for user-friendly tools and software that provide state-of-the-art approaches. Popular packages have reported limitations that will negatively affect results in applied studies. A critical recommendation is to improve the validity of topic modelling evaluation and align these measures with the needs of users. Moreover, the findings from this study will address the question of how to assess the established methodological robustness of topic modelling research.A SLR is a rigorous and practical approach to establishing the volume, significance, consistency and relevance of a specific selection of peer-reviewed literature, ensuring objective, accurate and reliable conclusions  literature databases. These databases were chosen based on their broad coverage of research subjects  are listed in Appendix 6. While the focus of the SLR is on short text topic modelling, social media platforms that allow for the creation of longer posts, such as Facebook and LinkedIn have been included in search strategy as posts on these platforms are typically far shorter than these character limits. The search was restricted to articles published between January 2016 and June 2021, capturing articles across a period of 5.5 years. A total of 1284 articles were retrieved.Articles were collected through searches of the Ebsco®, or that obtained a 2020 SNIP of 1.5 or higher. Conference papers were restricted to those that were ranked in CORE 2020 as A or Journal articles were restricted to those published in high-quality journals determined according to the SJRThis SLR sought to identify those articles where topic modelling was employed to investigate some phenomenon. Research that did not fit this description was excluded from this study. The complete list of exclusion and inclusion criteria are described in Table The screening process was conducted in two stages. All 738 titles and abstracts were read and evaluated against the inclusion criteria . In total, 44 final research categories were assigned. These categories were further aggregated into 17 categories and grouped under their research area according to the WoS schema.The analysis of this literature was conducted in two stages. In the first stage, all 189 articles were read, and the data necessary to map out the existing literature was extracted. In doing this, we could refine our existing line of enquiry further. Following this, we conducted a fine-grained analysis of 99 articles. We did not pursue a review of all articles as the trends in the data extraction remained stable, indicating that saturation had been reached and no new knowledge would be gained from a complete review Booth . This prDuring the period studied, publications have climbed dramatically, from 7 in 2016 to 65 in 2020. Given that 52 papers were published in the shortened 2021 six-month collection period, as shown in Fig. SMD studies that employ topic modelling are conducted throughout a range of research areas and disciplines See Fig. . A sizeaAlthough the two most prominent research areas were Social Sciences and Life Sciences and Biomedicine, in the last 18 months of the collection period (January 2020 to June 2021), the number of articles published in the Life Sciences and Biomedicine surpassed those published in the Social Sciences See Fig. . The mosCOVID-19 related studies made up 35.04%  published 29 articles 15.34%) followed by nine articles in PLoS One (4.76%), and five (2.65%) each in the International Journal of Disaster Risk Reduction and International Journal of Information Management. The journals with the most articles published are listed in Table 4% followMost studies used topic modelling to isolate manageable collections of semantically similar documents. Studies adopting a case study approach treat topics as artefacts of social media discourse that are anchored in the real world. These studies aim to draw inferences about a real-world environment based on the relationship between topics and other factors  of authors failed to declare the approach taken to tokenisation. A further 56.57% did not evidence that stemming or lemmatisation was conductedA small number of studies , four studies made use of two topic models. In these studies, LDA was combined with either Dirichlet multinomial mixture (DMM) model  Topic modelling developers should have familiarity with how topics are interpreted and the epistemologies and methodologies that guide interpretation. This will inform design of topic models, performance measures, and validation of new performance measures.(ii)Topic model research should, in many cases, not aim to target a breadth of settings but instead a well-defined set of applications, datasets and known interpretation protocols.(iii)Application-driven design and/or a demonstrative case study should be adopted. This includes the specification of a use case.Topic model research recommendations: ApproachesAspects of user knowledge that are lacking, present an opportunity for topic model developers to bridge this knowledge gap as part of their research design. A prominent theme throughout many of the studies was that researchers, aiming to adapt discipline-specific methodologies to incorporate topic modelling, were reliant on alternative, often manual methods, to achieve a result that could have been achieved through the use of an already available computational tool.A significant finding of this study was that LDA was used in the majority of studies (79.79%) even though it has been well documented in the empirical literature that LDA is sub-optimal for short texts such as social media Experimental studies must be conducted to bridge the gap between theoretical and applied work, as applied researchers may not understand the model behaviours responsible for the model output.(ii)Increased transparency of experimental settings, parameters, statistical presentation of performance, preprocessing and the limitations of novel topic models is required.(iii)Undertake interdisciplinary collaboration to benefit the construction and development of domain-specific methodological frameworks for applied researchers.(iv)Efforts should be made to investigate and explicitly articulate the limitations of a topic model within the context of an applied setting.(v)Developers should regularly engage with the applied literature to learn the needs of researchers using topic models and what is not working.Topic model research recommendations: User knowledge This systematic review aimed to provide insight into the applications of topic models. This information is useful to topic model developers to further understand the needs of those using topic modelling for their research, identify where research has underperformed when applied to real-world settings, and possible research gaps that require further attention. The section provides an overview of the areas for possible research advancement by both those who develop topic models and those who use them.The reviewed papers consistently stated that the validity of topic modelling as a research approach was challenging to promote  The validity of topic modelling should be addressed. The focus should be given to the development and validation of alternative performance measures which reflect the needs of researchers who are applying topic models to SMD.(ii)Alternative measures of performance that are in line with the needs and preferences of researchers applying topic models would be better suited as benchmark measures for the evaluation of new topic models.(iii)K.User-friendly implementation (tools and software) is required to ensure uptake of new models and approaches. Efforts should be made to make code more accessible. One example would be appropriate algorithms or at least methodological support to ‘select’ the number of topics (iv)Further investigation of the impacts of data features, preprocessing, and data quality on model performance is needed.Topic model research recommendations: Research advancement This SLR of existing literature on topic modelling applications for social media analysis, focused on how the topic modelling field can build on the literature from other disciplines. It defines several directions and recommendations for short text topic modelling research, particularly those geared towards social media investigations.To ensure effective uptake and application of topic modelling research in the future, we conclude that the field must participate and drive in the translation of its output to applied research. This could begin by developing a refined understanding of applied topic modelling intentions and broadening the empirical focus of the field’s research and their familiarity with the types of theoretical and epistemological frames through which topic models are interpreted. It should also expand its analytic capacity to address discipline-specific needs. Furthermore, there is ample room for topic modelling research to explicitly connect topic model development to contemporary applications of topic modelling structured around the various research paradigms by which applied work is conducted.The exponential increase in research that employs computational methods is significant. Medical informatics, public health, communications, information systems, and information sciences are among the fields where topic modelling research is highly valued. It is worth noting that topic modelling has the potential to drive clinically oriented research and, as a result, patient outcomes in the medical fields. This systematic review also discusses the implications for applied research. To be more specific, the sub-optimal practices should be addressed to bolster the validity and impact of applied topic modelling research. The clarification of these may aid practitioners in improving their research design, ultimately elevating the trustworthiness of computational methods. In this sense, our study directs topic modelling researchers to consider the critical capabilities required for impactful application of topic models and calls the attention of practitioners to those aspects of practice that may impede the success of topic modelling for social media analysis.One limitation of this study is that it may not provide a comprehensive picture of topic modelling applications. Given the recent explosion of peer-reviewed articles, the research design required inclusion criteria, which reduced the volume of potentially relevant literature reviewed. Despite this limitation, we believe that this SLR provides the promised visibility over applied topic modelling research practises for social media data in the cross-disciplinary literature. We hope our work inspires more systematic efforts to conduct application-driven research on topic modelling development.Supplementary file 1 (pdf 846 KB)Below is the link to the electronic supplementary material.


As editor of this Research Topic on neurobiology of addictions I am glad to present the interesting manuscripts that are finally included. I feel really grateful for the reviewers who have done a thoughtful job and for Frontiers that facilitates the process of producing a Research Topic enormously. But the ones that more strongly deserve my gratitude are the authors who not only shared their work with us but also trusted us to take care of it, review it and suggest changes.This Research Topic contains 10 manuscripts. There is no reason to believe that this sample is completely representative of the current scientific production on addiction neurobiology. Nevertheless, it is tempting to check how the focus of the manuscripts is distributed, and also serves to introduce them.mechanisms, the sample is varied. As expected, much of the research evaluates synaptic plasticity and neurotransmitter pathways. Nevertheless the studied pathways are really diverse and barely overlap. Dopamine is obviously there, but only one manuscript really focuses on dopaminergic pathways . Other neurotransmitter pathways that are explored in this topic are glutamate , and cannabinoid for drugs other than cannabinoids , while another evaluates the interaction of two pathways: sigma and adenosine .From the point of view of the rho GTPases  and Glycogen synthase kinase-3β . Some focus on the relationship between neurons from a point of view other than neurotransmitters, this is the case of the works focusing on neuronal excitability  and mRNA expression of cell adhesion molecules . Last but not the least, one manuscript is not focused on the mechanisms but is mainly behavioral, focusing on the preferences to different drug concentrations .Nevertheless some manuscripts do not focus on neurotransmitters. Other manuscripts focus on intracellular pathways: species, the two reviews include several species, one uses zebrafish larvae , two use mice  and all the rest use rats. This distribution of preclinical models seems to match the global trend but it also shows how when focusing on the internal mechanisms most of what we know about humans is extrapolated from animal models. There are obvious reasons why it is easier (and more efficient) to research neurotransmitter pathways, RNA expression or synapse structure in these animal models than in humans. But for the most part, the real target is to understand human addiction and the tools to investigate these mechanisms in human subjects are increasingly available: nuclear imaging, MRI connectivity, microRNA expression, and exosomes might be considered among the options.Regarding the drugs. Or is it not? Regarding the object of addiction, all of the manuscripts focus on substances. More concretely, each of them focus on a substance except for one which reviews a target for multiple substances . Interestingly all of the manuscript but one focus on substances usually classified as drugs: ethanol , nicotine , methamphetamine , and cocaine . The one not focused on a drug, focuses on sucrose . Sucrose is a common sugar in modern diets and is usually referred to as a nutrient and not a drug. Nevertheless, most of the population in the history of humankind had little or no access to sucrose in a purified form and most natural foods contain no sucrose or little sucrose. Sucrose has no direct effect on reward circuits, contrary to substances considered drugs. Accordingly, the work of Bimpisidis et al. fosters the understanding of reward processing in the absence of drugs, a paramount topic to understand behavioral addictions, notwithstanding its usefulness to understand the addictions in which sweets are involved. As a researcher myself, I have focused my previous work in addictions not directed toward drugs but toward behaviors. Some of their mechanisms may be shared with substance overuse disorder but the knowledge on the mechanisms of behavioral addictions lags behind that of the mechanisms of drug addiction. The burden of this kind of behavior is also high and possibly growing. But the knowledge of the knowledge of its intimate mechanisms lags behind that of drug addiction.Addiction is about Addictive Behaviors. It has also occurred despite my efforts to engage researchers focused on behavioral addictions (due to my aforementioned interest). As I stated above, this Research Topic, does not have to be a representative sample. And maybe the current research in the neurobiology of behavioral addictions is blooming somewhere else. But maybe the researchers focused on behavioral addictions are not the ones focused on neurobiology and vice versa.Yet, this Research Topic shows a great predominance of drug studies over studies targeting other objects of addiction. This predominance has occurred despite an inclusive Research Topic title: New Insights Into the Molecular Basis of Long-Term Plasticity Underlying Addiction and I hope this trend changes in the near future with more efforts to understand the neurobiology of behavioral addictions, and no decrease in the efforts to understand drug effects. In the meantime, I hope you find this Research Topic useful and enjoy the reading.The author confirms being the sole contributor of this work and has approved it for publication.


G be a graph of order n. Suppose k-th degree-based graph entropy for G is defined as k is real number. The first-degree-based entropy is generated for Graph entropy plays an essential role in interpreting the structural information and complexity measure of a network. Let  Graph theory has developed into a powerful mathematical tool in a wide range of disciplines, including operational research, chemistry, genetics, and linguistics, as well as electrical engineering, geography, sociology, and architecture. In addition, it has grown into a useful field of mathematics on its own. Using a diagram composed of a collection of points with lines connecting specific pairs of these points, many real-world situations can be simply explained. Chemists work with graphs on a daily basis because almost all chemistry interactions are carried out through the graphic representation of compounds and reactions. Chemical graph theory appears to be the natural language of chemistry through which chemists communicate. One of the important tools in this area is the graph invariant, which is any property of molecular graph that remains unchanged under graph isomorphism. Numerous kinds of graph invariants have appeared in the literature based on different graph parameters. One such important parameter is the degree of vertex, which is defined as the number of incident edges. For a molecular graph, it represents the valency of the corresponding atom. For degree-based invariants, readers are referred to the article  and the G based on For probability distribution , Dehmer  definedk-th degree-based graph entropy as followsk is real number. For Now, different entropy can be generated by varying o et al.  set ϕ models play an essential role in the design of purpose-specific fine chemicals such as pharmaceuticals. It is usually very costly to test a compound using a wet lab, but the QSPR study allows that cost to be reduced. Topological indices plays an important role in establishing structure property relationship of molecule. For some recent works on this analysis, readers are referred to ,19,20,21Topological indices abound and continue to grow in number. The majority of them are handled mathematically, lacking any sense of their chemical value. As a result, a collection of beneficial components was assembled to aid in picking of a pertinent molecular descriptor from a vast pool of candidates. Among the numerous qualities specified is the ability to anticipate the properties and activities of molecules. For the purpose of looking into the predicting ability of topological indices, quantitative structure–property relationship analysis is usually performed on theoretical attributes and experimental measures of some benchmark chemicals. The entropy-based indices are nurtured well in mathematical chemistry from a mathematical standpoint. Our aim is to illustrate the chemical connection of the entropy corresponding to the first Zagreb index. First, we consider the octane isomers as benchmark datasets. As octanes contain no cycles, we then take into account some hydrocarbons having cycles as a substructure.The molecular graph representations of octane isomers are displayed in S), enthalpy of vaporization , and the significance F   5) are sFor relation  takes foThe strength of structure property relationships  and 7),,7), is dS, S, From the in model  is compaThe relation  expresseplot see  ensure tF), inverse sum indeg index  for investigation. The molecular structures of BHCs are shown in The second-degree-based entropy is observed to correlate well with the boiling point (BP) of benzenoid hydrocarbons. The BP and The From relations  and 10)10), we cNow, we consider some molecular graphs having cyclic substructure which are useful in drug preparation. These compounds include Aminopterin, Aspidostomide E, Carmustine, Caulibugulone E, Convolutamine F, Convolutamydine A, Tambjamine K, Deguelin, Perfragilin A, Melatonin, Minocycline, Podophyllotoxin, Pterocellin B, Daunorubicin, Convolutamide A, Raloxifene. The molecular graphs of these structures are displayed in The Equations  and 12)12) reveaF, To compare the present descriptor with F. The absolute correlation coefficients of Now, to check the relationship of F indicates that there may be a strong mathematical connection between them, which could be considered as a future research direction.The impact of the entropy on structure property modeling corresponding to the first Zagreb index has been investigated in this work. The


We aimed to describe conditions of confinement among people incarcerated in the United States during the coronavirus disease 2019 (COVID-19) pandemic using a community-science data collection approach.We developed a web-based survey with community partners to collect information on confinement conditions . Formerly incarcerated adults released after March 1, 2020, or nonincarcerated adults in communication with an incarcerated person (proxy) were recruited through social media from July 25, 2020 to March 27, 2021. Descriptive statistics were estimated in aggregate and separately by proxy or formerly incarcerated status. Responses between proxy and formerly incarcerated respondents were compared using Chi-square or Fisher's exact tests based on α=0.05.Of 378 responses, 94% were by proxy, and 76% reflected state prison conditions. Participants reported inability to physically distance , inadequate access to soap (89%), water (46%), toilet paper (49%), and showers (68%) for incarcerated people. Among those receiving prepandemic mental health care, 75% reported reduced care for incarcerated people. Responses were consistent between formerly incarcerated and proxy respondents, although responses by formerly incarcerated people were limited.Our findings suggest that a web-based community-science data collection approach through nonincarcerated community members is feasible; however, recruitment of recently released individuals may require additional resources. Our data obtained primarily through individuals in communication with an incarcerated person suggest COVID-19 safety and basic needs were not sufficiently addressed within some carceral settings in 2020–2021. The perspectives of incarcerated individuals should be leveraged in assessing crisis–response strategies. Conditions, such as overcrowding, inadequate ventilation, poor sanitation, and difficulty accessing personal protective equipment (PPE), have created environments primed for outbreaks.Over 2 years after the initial global response to coronavirus disease 2019 (COVID-19), the U.S. sits a top two lists: the rate of incarceration and the rate of COVID-19 deaths.13 Despite the waning of the pandemic in the general population, conditions of confinement experienced by incarcerated populations can provide important insight toward planning humane responses to inevitable future infectious disease transmission.Additionally, prevalent chronic conditions  place incarcerated populations at greater risk of COVID-19-related morbidity and mortality.14 data indicate that mitigation strategies reported by state-level DOCs were irregularly implemented and punitive.16 Additionally, incarcerated individuals have reported inadequate access to soap, water, and disinfectant supplies,17 and extensive periods of lockdowns (suspension of activities and confinement restricted to housing areas) or solitary confinement  to achieve physical distancing.17–19While physical distancing, mask wearing, handwashing, and testing were recommended mitigation strategies by the Centers of Disease Control and Prevention (CDC) for carceral facilities,20–22 of which only two were from the United States.22 Yet, access to basic needs, medical care, and lockdown procedures were not examined. The unique perspective of directly impacted people, which has historically been ignored, is essential to the evaluation of emergent crisis response within carceral facilities. While community advocates identified the need for timely and wide-spread data on confinement conditions, obtaining research agreements from all U.S. carceral systems to directly survey currently incarcerated people was deemed a barrier to these urgent data needs during a rapidly changing public health emergency.While anecdotal evidence is compelling, robust quantitative data from the perspectives of incarcerated people during the pandemic are limited,Therefore, we partnered with community advocates to design and disseminate a web-based survey to collect detailed data on conditions of confinement and examine the feasibility of collecting data through outreach to community members such as loved ones, professional partners, and recently released individuals. Our methodological approach demonstrates how community members can contribute to epidemiological data collection efforts, the identification of salient health outcomes, and potential discordance between policy and implementation.14 community-informed concerns,23 anecdotal evidence, and surveys targeting the general population.In May 2020, a Community Advisory Board (CAB) was convened to examine conditions experienced by individuals incarcerated during the pandemic. The CAB included formerly incarcerated advocates from local and national organizations, Families for Justice as Healing, Justice 4 Housing, the National Council of Incarcerated and Formerly Incarcerated Women and Girls, and the Partakers Organization. Each CAB member requested the inclusion of statements of commitment to their community and organizational mission . At studThe instrument was piloted among 12 individuals who had been incarcerated and released after March 1, 2020 or were in communication with an incarcerated individual. The survey included questions regarding completion time, accessibility, and appropriateness of survey content. In partnership, we reviewed the pilot findings, implemented changes, and planned survey dissemination.The final instrument consisted of 42 questions, , or (2) had been in contact with someone currently incarcerated . To maximize reported data, questions could be skipped. Proxies in contact with multiple incarcerated individuals were asked to submit a separate survey for each person, unless proxies were legal/medical professionals, who were prompted to report the number of people for whom the information was applicable.The final study sample comprised 378 adults (≥18 years) who met the following criteria: (1) formerly incarcerated individual released after March 1, 2020 . We assessed feasibility by examining internal validity, completed questionnaires, sample size, and missingness. The distribution of responses  by proxies compared with FIRs for questions regarding COVID-19 safety, basic needs, and support were compared to assess internal validity. Individual results for FIRs should be carefully considered as the sample size is small.Comparisons were conducted using two-sided Fisher's exact tests based on an α=0.05. We also conducted sensitivity analyses to examine differences over time and geography for variables with inconsistencies between proxies and FIRs. All analyses were conducted using Stata/IC .This study was approved by the Institutional Review Board of the Mass General Brigham and deemed exempt because no personal identifiers were collected and the research posed minimal risk. All procedures were in accordance with institutional guidelines. However, we acknowledge that these data represent individuals who have been incarcerated and those who know them personally.n=357) were proxies who completed the survey based on experiences shared by someone currently incarcerated (n=21) were FIRs released after March 1, 2020. Most respondents provided data on facilities in the Northeast (37.0%), followed by the West (28.7%), South (26.1%), and Midwest (8.2%). In addition, 75.7% of respondents reflected conditions in state prisons, followed by jails (13.5%), federal prisons (5.8%), and other facilities, including immigration and customs enforcement (ICE) and juvenile detention centers (5.0%). Among respondents who reported gender (n=182) and race (n=163) of the impacted person, 39.6% were identified as female, 60.1% were White, 21.5% were Black, and 12.9% were Hispanic/Latinx.Of the 378 responses collected between July 28, 2020 and March 27, 2021, 94.4% . Additionally, participants reported that individuals were unable to take their belongings if moved due to COVID-19 . One proxy respondent shared,Lastly, >30% of respondents overall reported that facility-level information about COVID-19 was not shared .Overall, responses indicated inadequate access to basic necessities among incarcerated people . AlthougMy loved one recently got infected with COVID-19 and is also suffering for underlying medical conditions (chronic Hepatitis B and asthma). He didn't receive any medical treatments other than having his inhaler with him… The only “treatment” that was given to some individuals were over-the-counter medications such as Tylenol to reduce some flu-like symptoms. My loved one is still suffering from some lingering symptoms such as wheezing and coughing.Medical care and support resources were also affected by the pandemic response . A delaySimilarly, of those receiving mental health care before the pandemic, 96% of proxies and 89% of FIRs reported less or no care received. Access to legal services were also reportedly reduced  and parole hearings delayed (>30% in both groups).n=21; Questions about PPE replacement, increased stress, and receipt of financial support were asked only to FIRs n=21; . Eighty-It felt as if the jail sacrificing me to get covid by locking me down with a person who had Covid. They didn't care if I got sick or not and made no effort to separate me from the covid case.Being locked in a cell for 23 hours and 40 minutes a day for weeks at a time (whenever there was a report of a positive case among staff/incarcerated citizens was extremely stressful). I often considered hurting other prisoners or the guards simply because I was angry. I am not a violent person.Another FIR shared,There is a food shortage and many are not opting for mess hall. I am supporting 10 women & it's difficult and expensive.Questions about recency of communication, COVID-19 concerns, and support of incarcerated individuals were specifically asked of proxies. Most had communicated with the individual within the past week (85%) and reported increased expenses to support the individual (82%). One proxy respondent shared,A high proportion (70%) reported concerns about contracting the virus upon visitation reinstatement or from the incarcerated individual if released (50%).Overall, 23 surveys were initiated but not completed; however, response rates could not be calculated due to survey link distribution through social media and partner networks. Missingness was examined qualitatively to assess which variables were least likely to be answered by proxies or FIRs . Overallp>0.05) for all but two questions (p=0.03) and fewer proxies (30%) reported that incarcerated individuals had access to daily showers compared with 63% of FIRs (p=0.01). However, cautious interpretation is warranted due to the sample size limitations.The distribution of responses between proxies and FIRs were compared for key metrics and were found to be consistent . Respondents also indicated that incarcerated people had limited access to health care and legal aid. Lastly, the feasibility of this method was demonstrated by the ample sample size, and the comparability of findings to other sources of data ; however, resources would be needed to further engage formerly incarcerated individuals.26 Moreover, carceral systems are generally not mandated to publicly share data on the health of incarcerated people, impairing external oversight.27 Unsurprisingly, there are limited data examining the experiences of the incarcerated during the pandemic.20–22 Among the few studies available,20–22 only two addressed confinement conditions in the United States.22Understanding conditions of confinement during the COVID-19 pandemic from the perspective of incarcerated individuals is critical for crisis response planning and surveillance of standard operating procedures since public-facing policies may not align with actual implementation. Yet, incarcerated people have limited opportunities to challenge deviations from protocol.22 Our findings were similar to the CQCP study for some conditions, such as soap provision (67.9% vs. CQCP=70.6%) but differed for physical distancing (7.9% vs. CQCP=66.4%). This may be due to wording differences, such as the CQCP asking participants about the ability to maintain physical distancing “if possible” rather than “at all times.” Our study expands our understanding by increasing geographic variability, reflecting a longer time period (July 2020–March 2021 vs. April 2020–November 2020), and by including questions on lockdown, basic needs, and mental health care access.Between April and November 2020, the COVID-19 Questionnaire for Correctional Populations (CQCP) survey was administered to 327 individuals incarcerated in three U.S. states to collect data on mitigation strategies among staff and incarcerated individuals.21 In contrast to our findings, participants reported they felt more protected from COVID-19 than the general incarcerated population because of the solitary confinement measures they were subjected to, but reported infection was inevitable due to staff interactions and unsanitary food distribution. However, due to the small and highly selected sample, comparability of findings may be limited.Furthermore, a mixed methods study was conducted to understand the perceived risk of COVID-19 among 41 incarcerated males experiencing ≥20 h/day of solitary confinement.28 similar to 88% of FIRs in this study who worried about getting COVID-19 “most of the time.” Similarly, a survey among 50 individuals formerly detained by ICE reported that 80% were not able to maintain physical distance while eating, consistent with the 92% in our study who reported an inability to maintain physical distance.17Our findings also paralleled data published by the popular media. The Essie Justice Group conducted a survey of 729 people in contact with an incarcerated individual. Sixty-two percent reported the incarcerated individual feared losing their lives,16Discrepancies between CDC recommended mitigation strategies and data reported in this study  are furt29–31 including anxiety, depression, aggression, self-harm, and increased mortality postrelease,32 although it should be noted that empirical data are sparse and differences in study outcomes and design features limit consistency and generalizability of findings.33 Despite recommendations against long-term restrictive housing (>15 days) by the United Nations,34 U.S. Department of Justice,35 National Commission on Correctional Health Care,36 lockdowns have been a prioritized mitigation approach; however, the long-term consequences of its use during the pandemic and delays in medical and mental health care are uncertain.Furthermore, some mitigation strategies may lead to physical and psychological distress. Restrictive housing has been associated with psychological harm,Several limitations of this study should be considered. Generalizability of our findings may be limited and biased toward those with internet access and social media users. Responses were not uniformly geographically distributed due to the nature of our community partner networks; thus, results were not stratified by geographic location due to sample size considerations. Generalizability of our sample by race and ethnicity is also uncertain due to missing data for these variables. Although our sample may overrepresent conditions in female facilities given our partner networks, there is no evidence to suggest that conditions would be improved for incarcerated males or individuals of color. Furthermore, individual results for FIRs should be considered carefully given the small sample size. Despite these limitations, general comparisons with other studies support the observed patterns.Additionally, data from proxies may not be robust for all variables indicated by high proportions of missing data for some questions, such as parole hearings. For example, when comparing proxy to FIR responses, significantly more FIRs reported ability to shower daily if wanted, while more proxies reported lockdown conditions. However, 16% of FIRs who answered the questions regarding showering and lockdown were released before May 2020, before widespread response in all states, and were incarcerated in Florida, potentially reflecting variability in state responses. Although sensitivity analysis suggested high internal validity for other variables, these data should be interpreted cautiously due to sample size constraints.29 Thus, we could not assess the length of exposure to long-term lockdown or other COVID-19 mitigation strategies and findings may not reflect current conditions. Although we did not specifically exclude correctional staff, this group was not specifically engaged given recruitment through our community partner networks; however, their perspective is valuable and should be considered in future work.Finally, although we collected data on the date of release, we did not collect the duration of incarceration. Our data reflect protocols implemented within the 1st year of the pandemic; however, policies varied substantially across and within systems, were implemented at different periods of time during the pandemic and recommendations for mitigation shifted.Our data demonstrate that some incarcerated people reported experiencing inadequate implementation of safety protocols, difficulty accessing basic needs, and were subjected to long periods of restricted confinement. Further work is needed to confirm these findings in a representative sample. Our methods demonstrate the power of grassroots action to design a culturally relevant instrument and research study to collect descriptive, surveillance data of urgent community-identified needs. Data collected through these approaches can aid in addressing concerns of data transparency and oversight, and further support humane policy implementation, such as the use of decarceration, diversion programs, and compassionate release. Therefore, we urge public health leaders to engage with community organizations to develop meaningful research questions, leverage community strengths, and shape policy.


Molecular descriptors characterize the biological, physical, and chemical properties of molecules and have long been used for understanding molecular interactions and facilitating materials design. Some of the most robust descriptors are derived from geometrical representations of molecules, called 3-dimensional (3D) descriptors. When calculated from molecular dynamics (MD) simulation trajectories, 3D descriptors can also capture the effects of operating conditions such as temperature or pressure. However, extracting 3D descriptors from MD trajectories is non-trivial, which hinders their wide use by researchers developing advanced quantitative-structure–property-relationship models using machine learning. Here, we describe a suite of open-source Python-based post-processing routines, called PyL3dMD, for calculating 3D descriptors from MD simulations. PyL3dMD is compatible with the popular simulation package LAMMPS and enables users to compute more than 2000 3D molecular descriptors from atomic trajectories generated by MD simulations. PyL3dMD is freely available via GitHub and can be easily installed and used as a highly flexible Python package on all major platforms . A performance benchmark study used descriptors calculated by PyL3dMD to develop a neural network and the results showed that PyL3dMD is fast and efficient in calculating descriptors for large and complex molecular systems with long simulation durations. PyL3dMD facilitates the calculation of 3D molecular descriptors using MD simulations, making it a valuable tool for cheminformatics studies.The online version contains supplementary material available at 10.1186/s13321-023-00737-5. Molecular dynamics (MD) simulations are used to study the physical and chemical properties of materials . There aAlthough scientific studies and the development of novel materials have benefited from MD simulations using LAMMPS, the computational cost of atomistic methods still limits the size and time scale of the materials and processes that can be studied. In recent years, with the development of artificial intelligence, significant interest has arisen in machine learning (ML) as a powerful tool for the design and discovery of materials. This approach to predicting material properties is called quantitative-structure–property-relationship (QSPR) modeling and is becoming an essential technology in a wide variety of research fields because of its computational efficiency, scalability, robustness, and predictive ability –14.QSPR modeling is building mathematical relationships between material properties and molecular descriptors of the molecules that compose that material. Molecular descriptors are quantitative representations of physical, chemical, or topological characteristics of molecules that summarize our knowledge and understanding of molecular structure and activity from different aspects , 16. MolThere are currently thousands of molecular descriptors, which can be classified into three broad categories: 1D, 2D, and 3D descriptors, where D stands for dimension(s). Simple molecular descriptors derived by counting atom types or structural fragments in the molecule are called constitution or 1D descriptors. Descriptors derived from algorithms applied to a topological representation (molecular graph) are called topological or 2D descriptors. Lastly, there are molecular descriptors derived from geometrical representations of molecules called geometric or 3D descriptors . A descrVarious open-source and proprietary software packages have been developed to calculate descriptors, including PaDEL , BlueDesAlthough SMILES is easy and fast for calculating simple 1D and 2D descriptors by simple operations such as counting atom types or chemical fragments, it does not contain the information necessary for calculating 3D descriptors, such as the time-dependent geometries of molecules. For calculating 3D descriptors, the available molecular file formats include Sybyl MOL2 files  by Tripos, Inc., Sybyl Molfiles (.sm2) by ChemOffice, CambridgeSoft Corp., Multiple SD files (.sdf) by Molecular Design Ltd., HyperChem files (.hin) by Hypercube, Inc., MacroModel files by Schrodinger . These fIn contrast, MD simulations provide geometric information for multiple molecules and time frames. However, this information cannot be readily used by the currently available packages for calculating 3D descriptors. Specifically, with existing packages, the geometric information for each molecule at each timestep must be converted into the file format required by each package, resulting in the generation of many unnecessary files and computational inefficiency. In addition, most existing packages focus on calculations of simple 1D and 2D descriptors. Finally, available descriptor calculation packages do not directly accept the file and data structures that are output from typical MD simulations, for example, input data files (.lmp) and output trajectory files  from LAMMPS. Therefore, there is a need for a tool specifically oriented to 3D descriptors and MD simulations.Here, this need is addressed by a new Python package, PyL3dMD, where Py stands for Python, L for LAMMPS, 3d for 3-dimensional, and MD for molecular dynamics/descriptors. PyL3dMD in its current form can calculate 2066 3D molecular descriptors  in-silico molecules and materials [PyL3dMD is a robust computational tool capable of calculating more than 2000 3D descriptors , 44–79, aterials , 80–84. aterials , 44, 45,aterials , geometraterials –67, GETAaterials , 68, CPSaterials , 70, WHIaterials –74, RDF aterials , 3D-MoRSaterials , 77, andaterials , 3D Moraaterials , and 3D aterials ), facili3D topology descriptors are used to quantify the connectivity of atoms in three-dimensional space. These descriptors provide information about the bonds between atoms, the topology of the molecular surface, and the shape of the molecule , 44, 45.argparse to provide a command-line interface to pass inputs to the package, which allows multiple optional input parameters to be passed efficiently and for automation using shell scripts. PyL3dMD is coded in a module-oriented manner, where each set of descriptors is represented by a module. Furthermore, each module contains a driver function/module used to calculate the respective set of descriptors. This allows PyL3dMD not only to be used through the command line but also to be easily integrated into scripts for user-oriented analyses. Example scripts are provided in the supplementary materials. Owing to the modular structure of PyL3dMD, extensions or new descriptors can be implemented quickly and easily without time-consuming and complex modifications to the source code. To add a new descriptor, users only need to create a new function for that descriptor and call it in the main module. This provides users with the flexibility to either add a new descriptor that is currently unavailable in PyL3dMD or calculate a single descriptor from the existing six descriptor types, specifically for large systems. PyL3dMD has the following dependent Python packages: Math, Pandas, NumPy, Multiprocessing, Time, and Numba, which must be installed before using PyL3dMD. All these packages are by default installed with Anaconda and therefore PyL3dMD does not require any third-party package to be installed. The Numba package, which translates Python functions into optimized machine code at runtime, is used wherever it is possible to significantly increase the computation speed. Numba generates highly optimized machine code that executes much faster than the equivalent pure Python code [PyL3dMD is written in Python, which is readily available and allows for readable code. PyL3dMD uses hon code .An important consideration is the efficiency of the calculation, especially when the system consists of many molecules and trajectories containing data from many time frames. For computational efficiency, PyL3dMD uses matrix algebra wherever possible, rather than for/while loops. PyL3dMD can perform parallel computation for faster estimation of descriptors for all timesteps and molecules. Therefore, it can be used to calculate descriptors for any system size (number of atoms/molecules) irrespective of the complexity of the molecules. If run on desktop computers, PyL3dMD can automatically detect the number of cores available in the system and use them for multiprocessing.To calculate descriptors, the PyL3dMD package takes four inputs, two of which are mandatory, and two are optional, as shown in Fig. mentclass2pt{minimOn successful execution, PyL3dMD generates a comma-separated values (csv) file for each molecule in the simulation box consisting of molecular descriptors for all time frames. The naming convention for these csv files is molecule_molID.csv, where the molID is the ID of a molecule obtained from the LAMMPS output trajectory file. This way users have freedom to further post-process the calculated descriptors as needed, for example, averaging descriptors over time or multiple molecules.PyL3dMD is an intelligent parsing of the LAMMPS input data and output trajectory files. The order of sections  in the LAMMPS input data file can vary. PyL3dMD automatically detects each section to parse relevant information. In addition, LAMMPS allows users to export/write many parameters in any order  to the output trajectory file. PyL3dMD automatically determines the location of the parameters relevant to the calculation and sorts the coordinates using atom and molecule IDs, which allows users flexibility in the format of the output from LAMMPS.Another feature of PyL3dMD is that it works with any simulation box size with periodic boundaries, and for any box shape for which all sides of the box are perpendicular to one another, e.g., cubic and orthogonal boxes. LAMMPS allows users to export wrapped or unwrapped Moreover, PyL3dMD can be used with either united-atom (UA) or all-atom (AA) representations. The UA representation combines a group of atoms into a single "united atom" to simplify the simulation; the AA representation, on the other hand, treats each atom in a molecule individually, including hydrogen atoms. The package adds or removes hydrogen atoms depending on the descriptor type. In addition, PyL3dMD can be used for systems with single or multiple molecule types. For instance, PyL3dMD can calculate descriptors for a solution containing both polymer and solvent molecules .Lastly, PyL3dMD itself does not limit the size of the system (number of atoms/molecules) for the descriptor calculation but the maximum size that can be handled efficiently is dependent on the computing resources used.Regardless of the many advantages, PyL3dMD has several limitations currently.First, since the PyL3dMD package requires bond, angle, and dihedral information to calculate 3D topological and connectivity descriptors, the package only works for force fields that have this information in the LAMMPS input data file. The package has been tested with the force fields OPLS  CVFF 8787, and Ts ReaxFF . This iss ReaxFF ), atom cSecond, PyL3dMD assumes that the atom style is “full” when importing the atom section from the LAMMPS input data file, where “full” is one of the atoms styles for describing the information of each atom in the data file in LAMMPS following the order of “atom ID, molecule ID, atom type, charge, x, y, z”.3). However, this may not be an issue for QSPR modeling that is based on the relative values of descriptors for different molecules. The only system property that is calculated is density, so the units of density may need to be converted.Third, PyL3dMD assumes that the LAMMPS simulation is using the “real” unit system  is very large and the LAMMPS output trajectory file consists of over thousands of timesteps, the package should be run on a high-performance computing (HPC) cluster for faster computations. For such cases, PyL3dMD will work on a regular personal computer but might take longer computational time.Fig. 8 to C50) used here were obtained from the American Petroleum Institute (API) Research Project 42 [4 mPa8 to C50 indicates the complexity in the structure of these hydrocarbons. In total, 1248 data points for 305 hydrocarbons were used to develop the NN. These 1248 data points were randomly divided into three subsets: 70% to train the NN (training dataset), 15% to tune hyperparameters and architecture of the NN , and 15% to assess the performance of the final NN (test dataset) after it has been trained and tuned.As an example of the application of PyL3dMD, a Multiple-Input Multiple-Output (MIMO) neural network (NN) was developed for predicting temperature-dependent density and viscosity of wide variety of complex hydrocarbons using the molecular descriptors calculated from the PyL3dMD. The experimental dynamic viscosity and density of the hydrocarbons (Coject 42  over a w3). The interactions between atoms were described using the OPLS forcefield [In our previous study , which wrcefield , 92. EacIn our study , we alsoThe model architecture was defined to have multiple dense layers with ReLU activation. It was designed to have multiple input nodes representing the predictor/feature variables and multiple output nodes representing the target variables (density and viscosity). The network was compiled using the Adam optimizer and mean squared error as the loss function for each output, tracking mean absolute error (MSE) as a metric. Then, the network was trained using the training dataset. During the training step, MSE was minimized by adjusting the weights and biases of network. This was achieved through forward propagation and backpropagation, iteratively updating the model parameters using Adam optimizer. Then, using Bayesian optimization, the hyperparameters of NN, including number of neurons, hidden layers, activation functions, learning rate, epoch, and batch size were tuned to improve the model's performance and eliminate the overfitting.2) value, calculated for each hydrocarbon property in the training, validation, and test datasets. The Average Variance Inflation Factor (VIF) was also calculated to check for multicollinearity among the input variables. The average VIF for the selected descriptors was 5.2, which suggests that the predictors do not have concerning multicollinearity. Figures Finally, the performance of the final network was evaluated using the R-squared  were utilized to decipher the predictions made by the network. LIME, an ML technique, explains predictions of machine learning based models for individual data points . By compThe time required for the code to run is a function of the computational resource used (number of cores), system size (number of atoms in the simulation box), and the duration of the MD simulation (number of time frames). The code was tested for a simulation with 10 molecules having 20 atoms per molecule  with 1001 timesteps in the LAMMPS output trajectory file. The machine used for this analysis had the following configuration: Intel i7-10700 CPU with 8 cores, 16 processor, 32 GB RAM, and Windows 10 desktop. When multiprocessing with 8 cores was used, PyL3dMD took on average 16 min to calculate all descriptors whereas a single core took 30 min. Figure Then, the same benchmarking analysis was conducted for a larger simulation box, more realistic size using the same 8 core Windows system. For this benchmarking analysis, the simulation box of around 5000 atoms and LAMMPS output trajectory file of 3001 timesteps was considered which is same as the simulation box size using for 305 hydrocarbons. Windows desktop with 8 cores, PyL3dMD took 6.1, 14.8, 34.2, 139.9, 21.4, 51.0 min in calculating descriptors in the 3D Topological/Connectivity (set1), Geometric (set2), GETAWAY (set3), CPSA (set4), WHIM (set5), and Miscellaneous (set6) sets, respectively.Since we had to calculate molecular descriptors for 1248 MD simulations conducted at multiple temperatures, we used an HPC cluster with 72 CPU cores to speed up the computation. With the HPC, we were able to calculate molecular descriptors from 1248 large LAMMPS trajectory files (around 5000 atoms and 3001 timesteps) within hours. On average, it took 16.2 min for each simulation file of around 800 megabytes. The hydrocarbon molecules in the simulations had a range of sizes and structures, including n-paraffins, branched-paraffins, 1-olefin, branched-olefins, non-fused ring naphthene, fused ring naphthene, non-fused ring aromatic, and fused ring aromatics, and there was minimal effect of the molecular structure on compute time. This demonstrates the performance of PyL3dMD for large and complex molecular systems and confirms that PyL3dMD is a versatile tool that can be applied to material science studies without scale limitations, while also quantifying the effect of computing resources on computation speed.To benchmark the effect of number of cores/processors on the computational time of all the descriptors, we recorded the calculation time with 8, 20, 30, 40, 50, 60, and 72 processors. This analysis was conducted on the simulation box with around 5000 atoms  and LAMMPS trajectory files of 3001 timesteps. The recorder time for this simulation box with 8, 20, 30, 40, 50, 60, and 72 processors is shown in Fig. It is important to note that PyL3dMD does not impose any inherent limitations on the scale of the molecular system being analyzed. It is designed to calculate descriptors for simulation data of any size, including data generated from long simulation durations, large simulation boxes, and many molecules within the simulation box. The major factor that affects computation speed is the availability of computing resources for parallel computation. By leveraging parallel computing capabilities, PyL3dMD can effectively handle calculations on a large scale. In the future, we plan to enhance the coding architecture to significantly reduce computation time.If you identify any issues or have suggestions for improving PyL3dMD, we welcome you to reach out to us via email or GitHub. We value feedback, questions, and bug reports, and we are open to receiving them through both channels. We also encourage user contributions and invite you to submit a pull request on our GitHub page. This is particularly important as we aim to expand PyL3dMD's capabilities to calculate descriptors for other MD simulation tools.Currently, there are several commercial and open-source tools for calculating molecular descriptors, but none of them are compatible with MD simulation tools such as LAMMPS. Here, an open-source Python-based 3D molecular descriptors calculation tool, PyL3dMD, was developed, which is compatible with the formats of LAMMPS input data and output trajectory files. PyL3dMD has been published on GitHub, PyPi, and Conda under the GNU General Public License. PyL3dMD is a multithreaded tool able to utilize multiple CPU cores to increase the efficiency of descriptor calculations. There are two mandatory inputs (LAMMPS input data and output trajectory files) and one optional input (number of cores for multiprocessing) for calculating more than 2000 3D descriptors. PyL3dMD requires minimal user intervention but can also be easily expanded to include more descriptors. The package can be used on all major platforms, including Windows, Linux, and macOS, via Anaconda. In addition, the PyL3dMD package can be easily integrated into custom post-processing scripts. To demonstrate the application of molecular descriptors calculated from PyL3dMD, we developed and presented a multiple-input and multiple-output (MIMO) neural network (NN) to predict density and viscosity of hydrocarbons as functions of temperature. Results and performance benchmark show that PyL3dMD is a versatile tool that can be applied to material science studies with scale only limited by the availability of computing resources.In the future, PyL3dMD could undergo several improvements to enhance its functionality. These improvements may include increasing its compatibility with various atom styles , expanding the existing descriptor pool, optimizing computational efficiency, ensuring compatibility with other MD simulation packages other than just LAMMPS, and integrating ML algorithms to facilitate QSPR modeling by utilizing the calculated descriptors.This tool will enable scientific researchers to calculate a wide range of 3D descriptors to quantify molecular chemistry and structure, and ultimately guide the design of advanced materials.Additional file 1. User Manual for PyL3dMD.