dataesr/scientific-paragraphs-categorization huggingface dataset:
This indicated that more experienced GPs had less willingness to adapt their prescription strategies to guidelines and regulations, which should be considered in health politics given that physicians' mean age in France is more than 50 years old  [19]. The results presented here are in concordance with the initial results of the ZORRO study  [14, 15]  using a database of reimbursement claims of drugs, showing a decrease in French zolpidem consumers from 2. 79% to 1. 48%, with a fourfold increase in zolpidem discontinuation. With this database approach, different clusters of drug delivery after the new regulation of zolpidem prescription were also identified: the main proportion of patients (42%) continued zolpidem, which is consistent with the main strategy of no modification of zolpidem prescription found in our study.
However, the other magnitudes were different: zolpidem discontinuation without replacement was the second most frequent cluster in the database (31%), whereas it was a less frequent attitude expressed in our sample, and discontinuation with replacement (zopiclone or other BZDs) was the second most frequent attitude in our sample but not in the database approach (27%). This discrepancy may reflect divergent perceptions between GPs and patients; when the GPs thought to substitute zolpidem with another drug, patients seized the opportunity to stop hypnotic administration. Another possible explanation for this discrepancy is the different observation periods between the database and clinical approaches that can result in an initial replacement of zolpidem before a permanent stop of hypnotic administration.
The presence of psychiatric comorbidities has also been cited as a factor for choosing "conservative" strategies (no modification or a switch for another medication). Sleep disorders are common symptoms of psychiatric disorders such as mood disorders or anxiety disorders  [36]. Meanwhile, the presence of psychiatric disorders often needs curative treatment (as an antidepressant for major depressive disorder or generalized anxiety disorder) for patients, and hypnotics must be prescribed for a short period  [37]. The originality of our work lies in the investigation of GPs' perception of the new framework of zolpidem prescription, its link with the strategy of prescription employed and its complementarity with the database results of the previously published ZORRO study. Our sample is nationwide and has various profiles of GPs that reflect a diversity of practices.
New psychoactive substances (NPS) have become a serious threat for public health due to their ability to be sold in the street or on internet. NPS are either derived from commercial drugs which are misused (recreational rather than medical use) or whose structure is slightly modified. To regulate NPS, it is essential to accurately characterize them, either to recognize molecules that were previously identified or to quickly elucidate the structure of unknown ones. Most approaches rely on the determination of the exact mass obtained by high-resolution mass spectrometry requiring expensive equipment. This motivated us to develop a workflow in which the elucidation is assisted with databases and does not need the exact mass. This workflow combines 1D and 2D NMR measurements performed on a benchtop spectrometer with IR spectroscopy, for creating a multi-technique database to characterize pure and mixed NPS.
The experimental database was created with 57 entries mostly coming from seizures, mainly cathinones, cannabinoids, amphetamines, arylcyclohexylamines, and fentanyl. A blind validation of the workflow was carried out on a set of six unknown seizures. In the first three cases, AF, AB-FUBINACA, and a mixture of 2C-I and 2C-E could be straightforwardly identified with the help of their reference spectra in the database. The two next samples were elucidated for the first time with the help of the database to reveal NEK and MPHP substances. Finally, a precise quantification of each characterized NPS was obtained in order to track NPS trafficking networks.
Identification of known substances consists in comparing experimental data of a seized compound with reference data, which is quite straightforward. In contrast, elucidation is a more challenging task, necessary when identification fails and consisting in determining the structure of the unknowns in a sample from a set of combined analytical data. An efficient NMR-based approach named CASE (computerassisted spectral elucidation) has been developed for structure elucidation of small molecules. 4 CASE uses software to generate all possible molecular structures that are consistent with a particular set of 1D and 2D NMR data. However, CASE relies on the determination of the exact mass obtained by high-resolution mass spectrometry (HRMS), a technique which is not widely available in forensic science services.
This motivated us to develop a workflow in which the elucidation is assisted with databases and does not require prior knowledge of the exact mass. NPS characterization can be achieved by a variety of complementary analytical techniques. The first one is gas chromatography coupled to mass spectrometry (GC-MS), currently the most used in forensics. 5 MS combined with chemical ionization (CI) may be used to provide the molecular weight, but a relatively expensive HRMS instrumentation is required to access accurate molecular weight. MS combined with electron ionization (EI) mode provides information on different fragments of the molecule, and hyphenated MS/MS (or MS n ) delivers further insights into the fragmentation schemes of the molecule. 7] [8]  Unfortunately, these methods provide limited structural information.
1 T. These instruments can be installed on a benchtop, are easily transportable, and do not require maintenance (no cryogenic fluids). Nevertheless, benchtop NMR remains a young and underexplored technique for NPS characterization as compared with conventional high-field NMR. 9,10 Also, benchtop NMR has to face intrinsic sensitivity and resolution limits, which are illustrated in Figure  1  in the case of two common NPS. Such limitations make it difficult to only rely on benchtop NMR for structure identification or elucidation. Therefore, combinations with other methods like GC-MS or IR and/or with databases become a prerequisite for a reliable, fast, and automated characterization. Two F I G U R E 1 Spectrum of (a) 370mM AMB-FUBINACA in DMSO-d6 and (b) 306mM 2-BMMP in DMSO-d6 acquired at high-field (top: 400 MHz) and on a benchtop spectrometer (bottom: 60 MHz).
12 Next 432 seized samples were analyzed by NMR and GC-MS for cross-validation. 1  H NMR analysis nicely matched the GC-MS results with a 93% consistency rate. Another study reported the identification of drugs in two case samples, using a limited library of 12 spectra on an 80 MHz benchtop spectrometer, visually compared with data collected on a 600 MHz spectrometer. 13 In agreement with GC-MS analysis, the seized samples were found to contain morphine, acetyl codeine, MAM, and MDMA. In spite of these encouraging preliminary results, there is no general workflow incorporating benchtop NMR for the identification and elucidation of NPS structures. A first reason is that the above-mentioned examples were limited to 1D 1 H NMR spectroscopy and did not include 2D NMR experiments, which should provide a much higher degree of confidence in terms of structure characterization.
Moreover, the reported elucidation procedures rely on the determination of the exact mass as an initial step, but as explained above, HRMS instruments are uncommon in forensic laboratories. It is also important to note that the SWDRUGS group recommends using at least two techniques for unambiguous identification, which motivated us to combine benchtop NMR with another analytical approach. 14 tabases can be used with other analytical techniques such as IR. For instance, in a study by Jones et al. , 221 samples-most of them in mixtures-were screened using IR and Raman. 15 Authors compared the spectra to the database, and if a reference matched, they subtracted the reference spectra to the unknown one and made another comparison with the database. Only 41% of samples were unambiguously identified.
In this context, this work aims to develop and evaluate a general workflow based on optimized 1D and 2D NMR measurements performed on a benchtop spectrometer, combined with IR spectroscopy, for creating a multi-technique database allowing the characterization of pure and mixed NPS. Both identification and elucidation are considered in this workflow, which uses accessible analytical techniques while keeping in mind the demand of end-users in terms of reliability, robustness, and experiment time. After describing its implementation and the choice of the key parameters, the workflow was blind-tested on six real-seized samples, and the quantification capabilities of benchtop NMR were also assessed.
| Sample preparation Fifty-seven samples were used in this study. Five of them were purchased from Lipomed, seven samples were seized by the Finnish police, while all the others came from seizures by the French police. Also, six unknown seizures were obtained from the French police to allow validating the database. DMSO-d 6 purchased from Eurisotop with a purity of 99. 8% was used as an NMR solvent. TMS from Acros Organics with a purity of 99. 9% and TSP from Eurisotop with a purity of 99. 8% were used as chemical shift and concentration references, respectively. All seizures were dissolved in DMSO-d 6 to obtain a concentration as close as possible to 300 mM (see Table  S1 ), and 10 μl of TMS was added to calibrate 1 H and 13 C chemical shifts.
| NMR All the spectra were recorded at 26. 5 C using a 1 H,  19  F,  13  C 60 MHz Spinsolve spectrometer from Magritek equipped with a 20-position autosampler and a gradient coil in the direction of the static magnetic field B 0. The duration of 90 hard pulses was 14 μs at 0 dBW for 1 H, 60 μs at 0 dBW for  19  F, and 65. 9 μs at À14 dBW for  13  C. All acquisitions were made with the SpinsolveExpert software (v. 1. 41. 07). Moreover, an Avance III HD 700 MHz spectrometer with a 1 H/ 13 C/  15  N cryoprobe from Bruker was used to confirm the identification/ elucidation of the unknown NPS in samples. Three NMR pulse sequences were chosen for our study, 1D 1 H, 1D  19  F, and 2D 1 H-  13  C heteronuclear single quantum coherence (HSQC). The HSQC sequence was improved by optimization with the help of a test molecule, named 2-FDCK (see Table  S1 ).
Three variants of the HSQC sequence were tested, namely, Echo-Antiecho (EA) HSQC with WALTZ decoupling, phase sensitive (PH) HSQC with WALTZ decoupling, PH HSQC with Multiplicity Edition (ME), and WALTZ decoupling. PH HSQC was found in average twice more sensitive than EA HSQC (see S2). The PH HSQC ME (see S3) was found to be the most informative since it allowed determining the multiplicity n of 13 C( 1 H)n but this pulse sequence was 20% less sensitive than PH HSQC. The most important acquisitions parameters are gathered in Table  1. The NMR processing was performed with MestreNova™ software (version 14. 1). The 1D free induction decays were first multiplied by an exponential apodization function (0. 3 Hz line broadening) and zero-filled to a factor 4 then manually phased and baseline corrected with a Whittaker smoother algorithm.
| IR Analyses were carried out with a Bruker tensor 27 spectrometer in Attenuated Total Reflectance (ATR) mode. Each spectrum was acquired with 32 scans with a 400-4000 cm À1 spectral range and a 2 cm À1 spectral resolution. The processing of the spectra was achieved with the Opus™ (v8. 5) software. CO 2 , H 2 O compensation, baseline, and smoothing were applied to all IR spectra to obtain more reliable NPS IR fingerprints.
| ACD/Labs software All analytical data were gathered in the ACD/Labs software (version 2020. 1. 0 of July 15, 2020) from Advanced Chemistry Development, Inc (ACD/Labs). First, a project with all NMR experiments and the dedicated structure was created for each molecule and added to the database after peak picking and assignment of all signals and correlations. Then the corresponding IR spectrum was added to the database after an automatic peak picking.
| Database parameters Once the database has been created, two search methods are available in the ACD/Labs software to compare an unknown spectrum with one from the database. The first one, denoted "similarity search", compares the shape of the spectra only, disregarding peak picking and the multiplicity of the signals. The second one, called "peak searching", compares the peak picking of the NMR signal, an approach which is, obviously, significantly operator-dependent. With both search methods, the comparison between the experimental spectrum and the database yields a result called the Hit Quality Index (HQI) which quantifies the agreement between the database spectrums the most closely matching and the spectrum of the unknown compound. In the "similarity search" method, HQI can be calculated with two algorithms detailed below.
In both cases, the query spectrum is first indexed and split into N small regions. Next, all regions are integrated and, depending on the calculated integral, an index in the [À127;+127] range is assigned to each region. The first algorithm relies on the calculation of the Euclidian difference and treats each spectrum as a vector in a N-dimensional space. Thus, the resulting HQI is a measurement of the angle between the vectors giving a value between 0 (best match) and the square root of 2 (worst match). In the ACD/Labs software, this value is next scaled to deliver a value between 0 (worst match) and 100 (best match) for the HQI display: HQI ¼ 1 N Â 127 ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi X L i¼1 abs p EXP i À p DB i À Á 2 r , ð1Þ with L the compared regions, N the number of used indexes, and p i the index from the experimental (EXP) spectrum and the database spectrum (DB).
The second algorithm relies on the calculation of the absolute distance based on the difference for each data point with respect to the total area under the curve (i. e. , the sum of all the intensities). In other words, each data point represents a percentage of the total area. Thus, the larger the intensity value of a data point, the larger its weight: HQI ¼ 1 N Â 127 X L i¼1 j p EXP i À p DB i À Á j : ð2Þ Only one HQI algorithm is available for "peak searching" calculated as follows: HQI ¼ 1 L X L k¼1 N k M k Â 100%, ð3Þ with L the total number of peak regions in the experimental spectrum, N k the number of peaks in the k-peak region of the experimental spectrum, and M k the number of peaks in the k-peak region of the DB spectrum. Finally, for improving the database search, two parameters can be chosen by the operator.
The first one is the HQI threshold value under which the results are filtered out. The second one, only available for "peak searching", is the looseness factor (LF) that allows considering the variability in the chemical shifts of the different peaks obtained in NMR. LF corresponds to the highest difference in ppm which can be accepted to provide a match between the experimental spectra and the database (both in 1D and 2D). T A B L E.
| Multi-technique workflow The choice of the analytical workflow is essential regarding the complexity of the sample embedding NPS. The strategy to build for identifying and elucidating NPS in seized samples has to face difficulties met to detect and quantify NPS even in mixture. For a more comprehensive and integrated drug investigation, different multi-methods workflows have been reported and allow for elucidating NPS  11. They all combine multi-analytical techniques like Raman, DSC, IR, NMR, MS, and coupled chromatographic techniques with chemoinformatic tools and commercial, in-house databases for assisting spectral assignment, identification, and elucidation. An applicative case where seized blotter paper  20  was analyzed by GC-MS, UHPLC-HRMS as well as 1D and 2D NMR in combination with multi-methods chemometric tools like ACD/Labs, Mass Hunter, and XCalibur.
This workflow led to the identification of a LSD homologous product. In the case of seized NPS in mixture with herbs, the same workflow was utilized after NPS extraction and successfully revealed a new synthetic cannabinoid. 18 nce high-field NMR and HRMS are expensive instruments and not owned by a majority of forensic laboratories, there is a need for cheaper and more accessible alternative workflows. We propose 1D and 2D optimized experiments implemented on liquid-state benchtop instruments in combination with IR and dedicated associated databases to identify and also elucidate NPS. Note that our approach aims at being quite general and may not be adapted to NPS hidden or sprayed in materials that would require a more specific strategy. For our approach, an experimental database was created with 57 entries, mainly coming from cathinones, cannabinoids, amphetamines, arylcyclohexylamines, and fentanyloids (see Table  S1 ).
Before elaborating the multi-technique workflow, three model molecules were used to determine the best search method, the optimum algorithm for HQI calculation, the most adapted HQI threshold, as well as the optimum LF value. Table  2  summarizes the optimized parameters, details of the optimization process being provided in the SI (see S4 and S5). As described above, the workflow starting point is the 1D  19  F analysis to determine if the molecule possesses one or several  19  F (Figure  2 ). Then, comparison between the measured HSQC spectrum and the database is carried out. The result of this comparison dictates the choice between the identification and elucidation parts of the workflow. When the HSQC pulse sequence gives a single match for the unknown structure, the leftmost part of the workflow in Figure  2  is used, corresponding to identification.
| Database validation on real cases A blind validation of the workflow was carried out on a set of six unknown seizures (numbered from 1 to 6) to evaluate if the database would be able to cope with concrete cases and could be routinely  These first three examples highlight the efficiency of the identification component of the workflow since the database was able to systematically provide the structure of NPS obtained from seizures. In addition, the identification of a (simple) mixture could be achieved thanks to our workflow. The three next samples correspond to cases where the elucidation part of the workflow was applied, because the identification failed to provide a clear hit. As illustrated in Figure  3 , sample no. 4 provided no signal in 1D  19  F spectrum, and we moved directly to the HSQC comparison.
No match could be obtained using a LF of 0. 1 ppm for 1 H and 1 ppm for  13  C. Next, we tried to find structures that would be close to the unknown one (elucidation by homology) by comparing the experimental HSQC spectrum with the database while concomitantly incrementing LF by steps of 0. 1 ppm for 1 H and 1 ppm for  13  C. With such procedure, we obtained O-PCE as a first match, with a LF of 0. 6 ppm for 1 H and 6 ppm for  13  C. Then, a 1D 1 H comparison was performed and the first two structures were O-PCE (91. 04) and MXE (87. 33). All 1 H signals had been integrated, and we found that the aromatic region integrated to four protons in agreement with a di-substituted phenyl ring, with no major modification of the spectral pattern between the unknown structure and O-PCE. Therefore, we attempted to better clarify the nature of substituting group on the aromatic region with the IR experiment.
IR comparison was also made, and the same structures as before were identified as the closest to our structure. Finally, the HSQC prediction was done considering a butyl side chain (3 CH 2 and 1 CH 3 ) and it returned a map closely resembling one of the unknown samples (Figure  S10a ). Thus, the structure was identified as MPHP with a butyl side chain, and this was confirmed by 700 MHz experiments (see S10). The two previous cases demonstrate that elucidation with the help of a database can be carried out relying on benchtop NMR combined with IR spectroscopy. The two new structures were eventually added to the database. Indeed, the database can be improved while previously unknown NPS are discovered, allowing a continuous improvement of its identification and elucidation abilities. For the last case, sample no.
6, the usual workflow was followed. The 1D  19  F comparison was not performed since no fluorine signal could be observed on the spectrum. Then, the comparison with HSQC provided no match with the LF factors given in Table  2. Therefore, LF was incremented until a match was obtained with a LF of 0. 5 for 1 H and 5 ppm for  13  C. The proposed structure with this comparison is NEK, the sample that was added to the database just after processing sample no. 4. To confirm that structure, 1D 1 H comparison was used and provided NEK (92. 50), O-PCE (89. 72), and HEXEN (86. 56) as the three best matches. However, when spectral patterns of those two spectra were compared, it could be seen that additional signals were present in sample no. 6 (see S11). The presence of a mixture explains why LF needed to be increased in order to obtain a match with HSQC.
The two components of this mixture were determined and validated at 700 MHz (see S11): It turns out that the sample is a mixture of NEK and MDMC. This shows that although benchtop NMR failed to identify all the analytes in the mixture, the major component (NEK) could be identified relying on our workflow. To summarize, six cases were examined using the previously constructed workflow. In the first three cases, AF, AB-FUBINACA, and a mixture of 2C-I and 2C-E, were straightforwardly identified with the help of their reference spectra in the database. The two next samples were elucidated with the help of the database to determine the structures of NEK and MPHP (see S10 and S11). Finally, the last sample could not be fully characterized by our workflow which nevertheless identified NEK as the major component of the mixture.
| Quantification To complete our study, the purity of the fully characterized NPS was measured by quantitative benchtop NMR. Purity determination is an additional data to trace the manufacturers and to further dismantle sis, but these techniques require finding the corresponding reference material, which is not systematically accessible and can also be prohibitively expensive. Therefore, NMR appears as a valuable alternative because, on the one hand, quantification can be achieved without physical separation of the sample components, and on the other hand, multiple compounds can be precisely quantified with a single internal or external reference, which does not necessarily have to be close to the analyte (in contrast to chromatography). An internal reference was not used here since avoiding overlap with NMR signals was beyond reach for the benchtop apparatus.
| CONCLUSION An integrative workflow was created to help identifying NPS. It includes benchtop 1D and 2D NMR, IR, and prediction together with an experimental database to characterize NPS according to the directives of the SWGDRUGS workgroup. The database includes data for 57 compounds but will be enriched with more samples in the near future. The efficiency of the workflow was evaluated on six seizures. Most structures were unambiguously identified or elucidated, even in the case of mixtures. This evaluation also highlighted the limitation of the workflow when significant overlap between peaks occurs. In some cases, an expert or habituated eye will most likely be needed, but we are confident that the workflow presented in this study would facilitate the identification process. Additionally, purity was also determined for each sample thanks to 1D 1 H NMR.